
!pip install kaggle

Requirement already satisfied: kaggle in /usr/local/lib/python3.9/site-packages (1.5.12)
Requirement already satisfied: tqdm in /usr/local/lib/python3.9/site-packages (from kaggle) (4.62.3)
Requirement already satisfied: python-slugify in /usr/local/lib/python3.9/site-packages (from kaggle) (5.0.2)
Requirement already satisfied: urllib3 in /usr/local/lib/python3.9/site-packages (from kaggle) (1.26.7)
Requirement already satisfied: python-dateutil in /usr/local/lib/python3.9/site-packages (from kaggle) (2.8.2)
Requirement already satisfied: certifi in /usr/local/lib/python3.9/site-packages (from kaggle) (2021.10.8)
Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.9/site-packages (from kaggle) (1.15.0)
Requirement already satisfied: requests in /usr/local/lib/python3.9/site-packages (from kaggle) (2.26.0)
Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.9/site-packages (from python-slugify->kaggle) (1.3)
Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/site-packages (from requests->kaggle) (2.0.4)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/site-packages (from requests->kaggle) (3.3)
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
WARNING: You are using pip version 21.3.1; however, version 23.0.1 is available.
You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.


!pwd

/root/shared/Coursesv2/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2


!pwd

/root/shared/Coursesv2/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2


!ls -l  /root/shared/Coursesv2/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2/kaggle.json

-rw-r--r-- 1 root root 63 Apr 10 22:36 /root/shared/Coursesv2/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2/kaggle.json


# !mkdir /.kaggle
!cp kaggle.json ~/.kaggle
# # !chmod 600 ~/.kaggle/kaggle.json


! kaggle competitions files home-credit-default-risk

Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'
Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.13 / client 1.5.12)
name                                 size  creationDate         
----------------------------------  -----  -------------------  
previous_application.csv            386MB  2019-12-11 02:55:35  
credit_card_balance.csv             405MB  2019-12-11 02:55:35  
installments_payments.csv           690MB  2019-12-11 02:55:35  
HomeCredit_columns_description.csv   37KB  2019-12-11 02:55:35  
application_train.csv               158MB  2019-12-11 02:55:35  
bureau_balance.csv                  358MB  2019-12-11 02:55:35  
application_test.csv                 25MB  2019-12-11 02:55:35  
bureau.csv                          162MB  2019-12-11 02:55:35  
POS_CASH_balance.csv                375MB  2019-12-11 02:55:35  
sample_submission.csv               524KB  2019-12-11 02:55:35


DATA_DIR = "/root/shared/Coursesv2/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2"   #same level as course repo in the data directory
#DATA_DIR = os.path.join('./ddddd/')
!mkdir DATA_DIR

mkdir: cannot create directory ‘DATA_DIR’: File exists


!ls -l DATA_DIR

total 2621364
-rw-r--r-- 1 root root     37383 Apr 10 23:05 HomeCredit_columns_description.csv
-rw-r--r-- 1 root root 392703158 Apr 10 23:05 POS_CASH_balance.csv
-rw-r--r-- 1 root root  26567651 Apr 10 23:05 application_test.csv
-rw-r--r-- 1 root root 166133370 Apr 10 23:05 application_train.csv
-rw-r--r-- 1 root root 170016717 Apr 10 23:05 bureau.csv
-rw-r--r-- 1 root root 375592889 Apr 10 23:05 bureau_balance.csv
-rw-r--r-- 1 root root 424582605 Apr 10 23:05 credit_card_balance.csv
-rw-r--r-- 1 root root 723118349 Apr 10 23:06 installments_payments.csv
-rw-r--r-- 1 root root 404973293 Apr 10 23:06 previous_application.csv
-rw-r--r-- 1 root root    536202 Apr 10 23:06 sample_submission.csv


! kaggle competitions download home-credit-default-risk -p $DATA_DIR --force

Downloading home-credit-default-risk.zip to /root/shared/Coursesv2/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2
100%|███████████████████████████████████████▉| 688M/688M [01:26<00:00, 7.51MB/s]
100%|████████████████████████████████████████| 688M/688M [01:26<00:00, 8.36MB/s]


# !chmod 600 /root/.kaggle/kaggle.json


!pwd

/root/shared/Coursesv2/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2


!ls -l $DATA_DIR

total 714032
drwxr-xr-x 1 root root       512 Apr 10 23:01 Coursesv2
drwxr-xr-x 1 root root       512 Apr 10 23:08 DATA_DIR
-rwxrwxrwx 1 root root   7014975 Apr 11 21:56 HCDR_baseLine_submission_with_numerical_and_cat_features_to_kaggle.ipynb
-rwxrwxrwx 1 root root        11 Feb 21 05:06 Phase2.md
-rw-r--r-- 1 root root 721616255 Apr 11 21:57 home-credit-default-risk.zip
-rwxrwxrwx 1 root root     66899 Feb 21 05:06 home_credit.png
-rw-r--r-- 1 root root        63 Apr 10 22:36 kaggle.json
-rwxrwxrwx 1 root root   1368981 Feb 21 05:06 submission.csv
-rwxrwxrwx 1 root root   1091396 Feb 21 05:06 submission.png


#!rm -r  DATA_DIR


import numpy as np
import pandas as pd 
from sklearn.preprocessing import LabelEncoder
import os
import zipfile
from sklearn.base import BaseEstimator, TransformerMixin
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline, FeatureUnion
from pandas.plotting import scatter_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
import warnings
warnings.filterwarnings('ignore')


unzippingReq = True #True
if unzippingReq: #please modify this code 
    zip_ref = zipfile.ZipFile(f'{DATA_DIR}/home-credit-default-risk.zip', 'r')
    # extractall():  Extract all members from the archive to the current working directory. path specifies a different directory to extract to
    zip_ref.extractall('DATA_DIR') 
    zip_ref.close()


ls -l ../../../Data/home-credit-default-risk/application_train.csv

ls: cannot access '../../../Data/home-credit-default-risk/application_train.csv': No such file or directory


!ls -l Coursesv2/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2/DATA_DIR/application_train.csv

ls: cannot access 'Coursesv2/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2/DATA_DIR/application_train.csv': No such file or directory


import numpy as np
import pandas as pd 
from sklearn.preprocessing import LabelEncoder
import os
import zipfile
from sklearn.base import BaseEstimator, TransformerMixin
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline, FeatureUnion
from pandas.plotting import scatter_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
import warnings
warnings.filterwarnings('ignore')

def load_data(in_path, name):
    df = pd.read_csv(in_path)
    print(f"{name}: shape is {df.shape}")
    print(df.info())
    display(df.head(5))
    return df

datasets={}  # lets store the datasets in a dictionary so we can keep track of them easily
ds_name = 'application_train'
DATA_DIR = "/root/shared/Coursesv2/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2/DATA_DIR"
datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)

datasets['application_train'].shape

application_train: shape is (307511, 122)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB
None

(307511, 122)


DATA_DIR

'/root/shared/Coursesv2/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2/DATA_DIR'


ds_name = 'application_test'
datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)

application_test: shape is (48744, 121)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 48744 entries, 0 to 48743
Columns: 121 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(40), object(16)
memory usage: 45.0+ MB
None


%%time
ds_names = ("application_train", "application_test", "bureau","bureau_balance","credit_card_balance","installments_payments",
            "previous_application","POS_CASH_balance")

for ds_name in ds_names:
    datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)

application_train: shape is (307511, 122)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB
None

application_test: shape is (48744, 121)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 48744 entries, 0 to 48743
Columns: 121 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(40), object(16)
memory usage: 45.0+ MB
None

bureau: shape is (1716428, 17)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1716428 entries, 0 to 1716427
Data columns (total 17 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_CURR              int64  
 1   SK_ID_BUREAU            int64  
 2   CREDIT_ACTIVE           object 
 3   CREDIT_CURRENCY         object 
 4   DAYS_CREDIT             int64  
 5   CREDIT_DAY_OVERDUE      int64  
 6   DAYS_CREDIT_ENDDATE     float64
 7   DAYS_ENDDATE_FACT       float64
 8   AMT_CREDIT_MAX_OVERDUE  float64
 9   CNT_CREDIT_PROLONG      int64  
 10  AMT_CREDIT_SUM          float64
 11  AMT_CREDIT_SUM_DEBT     float64
 12  AMT_CREDIT_SUM_LIMIT    float64
 13  AMT_CREDIT_SUM_OVERDUE  float64
 14  CREDIT_TYPE             object 
 15  DAYS_CREDIT_UPDATE      int64  
 16  AMT_ANNUITY             float64
dtypes: float64(8), int64(6), object(3)
memory usage: 222.6+ MB
None

bureau_balance: shape is (27299925, 3)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27299925 entries, 0 to 27299924
Data columns (total 3 columns):
 #   Column          Dtype 
---  ------          ----- 
 0   SK_ID_BUREAU    int64 
 1   MONTHS_BALANCE  int64 
 2   STATUS          object
dtypes: int64(2), object(1)
memory usage: 624.8+ MB
None

credit_card_balance: shape is (3840312, 23)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3840312 entries, 0 to 3840311
Data columns (total 23 columns):
 #   Column                      Dtype  
---  ------                      -----  
 0   SK_ID_PREV                  int64  
 1   SK_ID_CURR                  int64  
 2   MONTHS_BALANCE              int64  
 3   AMT_BALANCE                 float64
 4   AMT_CREDIT_LIMIT_ACTUAL     int64  
 5   AMT_DRAWINGS_ATM_CURRENT    float64
 6   AMT_DRAWINGS_CURRENT        float64
 7   AMT_DRAWINGS_OTHER_CURRENT  float64
 8   AMT_DRAWINGS_POS_CURRENT    float64
 9   AMT_INST_MIN_REGULARITY     float64
 10  AMT_PAYMENT_CURRENT         float64
 11  AMT_PAYMENT_TOTAL_CURRENT   float64
 12  AMT_RECEIVABLE_PRINCIPAL    float64
 13  AMT_RECIVABLE               float64
 14  AMT_TOTAL_RECEIVABLE        float64
 15  CNT_DRAWINGS_ATM_CURRENT    float64
 16  CNT_DRAWINGS_CURRENT        int64  
 17  CNT_DRAWINGS_OTHER_CURRENT  float64
 18  CNT_DRAWINGS_POS_CURRENT    float64
 19  CNT_INSTALMENT_MATURE_CUM   float64
 20  NAME_CONTRACT_STATUS        object 
 21  SK_DPD                      int64  
 22  SK_DPD_DEF                  int64  
dtypes: float64(15), int64(7), object(1)
memory usage: 673.9+ MB
None

installments_payments: shape is (13605401, 8)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13605401 entries, 0 to 13605400
Data columns (total 8 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_PREV              int64  
 1   SK_ID_CURR              int64  
 2   NUM_INSTALMENT_VERSION  float64
 3   NUM_INSTALMENT_NUMBER   int64  
 4   DAYS_INSTALMENT         float64
 5   DAYS_ENTRY_PAYMENT      float64
 6   AMT_INSTALMENT          float64
 7   AMT_PAYMENT             float64
dtypes: float64(5), int64(3)
memory usage: 830.4 MB
None

previous_application: shape is (1670214, 37)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
 #   Column                       Non-Null Count    Dtype  
---  ------                       --------------    -----  
 0   SK_ID_PREV                   1670214 non-null  int64  
 1   SK_ID_CURR                   1670214 non-null  int64  
 2   NAME_CONTRACT_TYPE           1670214 non-null  object 
 3   AMT_ANNUITY                  1297979 non-null  float64
 4   AMT_APPLICATION              1670214 non-null  float64
 5   AMT_CREDIT                   1670213 non-null  float64
 6   AMT_DOWN_PAYMENT             774370 non-null   float64
 7   AMT_GOODS_PRICE              1284699 non-null  float64
 8   WEEKDAY_APPR_PROCESS_START   1670214 non-null  object 
 9   HOUR_APPR_PROCESS_START      1670214 non-null  int64  
 10  FLAG_LAST_APPL_PER_CONTRACT  1670214 non-null  object 
 11  NFLAG_LAST_APPL_IN_DAY       1670214 non-null  int64  
 12  RATE_DOWN_PAYMENT            774370 non-null   float64
 13  RATE_INTEREST_PRIMARY        5951 non-null     float64
 14  RATE_INTEREST_PRIVILEGED     5951 non-null     float64
 15  NAME_CASH_LOAN_PURPOSE       1670214 non-null  object 
 16  NAME_CONTRACT_STATUS         1670214 non-null  object 
 17  DAYS_DECISION                1670214 non-null  int64  
 18  NAME_PAYMENT_TYPE            1670214 non-null  object 
 19  CODE_REJECT_REASON           1670214 non-null  object 
 20  NAME_TYPE_SUITE              849809 non-null   object 
 21  NAME_CLIENT_TYPE             1670214 non-null  object 
 22  NAME_GOODS_CATEGORY          1670214 non-null  object 
 23  NAME_PORTFOLIO               1670214 non-null  object 
 24  NAME_PRODUCT_TYPE            1670214 non-null  object 
 25  CHANNEL_TYPE                 1670214 non-null  object 
 26  SELLERPLACE_AREA             1670214 non-null  int64  
 27  NAME_SELLER_INDUSTRY         1670214 non-null  object 
 28  CNT_PAYMENT                  1297984 non-null  float64
 29  NAME_YIELD_GROUP             1670214 non-null  object 
 30  PRODUCT_COMBINATION          1669868 non-null  object 
 31  DAYS_FIRST_DRAWING           997149 non-null   float64
 32  DAYS_FIRST_DUE               997149 non-null   float64
 33  DAYS_LAST_DUE_1ST_VERSION    997149 non-null   float64
 34  DAYS_LAST_DUE                997149 non-null   float64
 35  DAYS_TERMINATION             997149 non-null   float64
 36  NFLAG_INSURED_ON_APPROVAL    997149 non-null   float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB
None

POS_CASH_balance: shape is (10001358, 8)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10001358 entries, 0 to 10001357
Data columns (total 8 columns):
 #   Column                 Dtype  
---  ------                 -----  
 0   SK_ID_PREV             int64  
 1   SK_ID_CURR             int64  
 2   MONTHS_BALANCE         int64  
 3   CNT_INSTALMENT         float64
 4   CNT_INSTALMENT_FUTURE  float64
 5   NAME_CONTRACT_STATUS   object 
 6   SK_DPD                 int64  
 7   SK_DPD_DEF             int64  
dtypes: float64(2), int64(5), object(1)
memory usage: 610.4+ MB
None

CPU times: user 30.7 s, sys: 11.2 s, total: 42 s
Wall time: 52.3 s


for ds_name in datasets.keys():
    print(f'dataset {ds_name:24}: [ {datasets[ds_name].shape[0]:10,}, {datasets[ds_name].shape[1]}]')

dataset application_train       : [    307,511, 122]
dataset application_test        : [     48,744, 121]
dataset bureau                  : [  1,716,428, 17]
dataset bureau_balance          : [ 27,299,925, 3]
dataset credit_card_balance     : [  3,840,312, 23]
dataset installments_payments   : [ 13,605,401, 8]
dataset previous_application    : [  1,670,214, 37]
dataset POS_CASH_balance        : [ 10,001,358, 8]


datasets["application_train"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB


datasets["application_train"].describe() #numerical only features


datasets["application_test"].describe() #numerical only features


datasets["application_train"].describe(include='all') #look at all categorical and numerical


datasets["application_test"].describe(include='all') #look at all categorical and numerical


percent = (datasets["application_train"].isnull().sum()/datasets["application_train"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["application_train"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Train Missing Count"])
missing_application_train_data.head(20)


pip install missingno

Requirement already satisfied: missingno in /usr/local/lib/python3.9/site-packages (0.5.2)
Requirement already satisfied: scipy in /usr/local/lib/python3.9/site-packages (from missingno) (1.7.3)
Requirement already satisfied: seaborn in /usr/local/lib/python3.9/site-packages (from missingno) (0.11.2)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.9/site-packages (from missingno) (3.4.3)
Requirement already satisfied: numpy in /usr/local/lib/python3.9/site-packages (from missingno) (1.22.0)
Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.9/site-packages (from matplotlib->missingno) (3.0.6)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/site-packages (from matplotlib->missingno) (0.11.0)
Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.9/site-packages (from matplotlib->missingno) (2.8.2)
Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.9/site-packages (from matplotlib->missingno) (9.0.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/site-packages (from matplotlib->missingno) (1.3.2)
Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.9/site-packages (from seaborn->missingno) (1.3.5)
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.9/site-packages (from pandas>=0.23->seaborn->missingno) (2021.3)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib->missingno) (1.15.0)
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
WARNING: You are using pip version 21.3.1; however, version 23.0.1 is available.
You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.
Note: you may need to restart the kernel to use updated packages.


import missingno as missingnum


# missingnum.matrix(datasets["application_train"], color=(0.2980392156862745, 0.4470588235294118, 0.6901960784313725))

<AxesSubplot:>


#providing figure size details

plt.figure(figsize=(17, 8))
#sorting values to be plotted

missing_application_train_data['Percent'].sort_values().tail(60).plot.barh(figsize=(15, 30), color='pink')
plt.title("Top 60 variables with the highest percentage of missing values in the application train data")
plt.grid(True)
plt.show();


percent = (datasets["application_test"].isnull().sum()/datasets["application_test"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["application_test"].isna().sum().sort_values(ascending = False)
missing_application_test_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_test_data.head(20)


plt.figure(figsize=(17, 7))
missing_application_test_data['Percent'].sort_values().tail(60).plot.barh(figsize=(17, 35), color='purple')
plt.title("Top 60 variables with the highest percentage of missing values in the application test data")
plt.grid(True)
plt.show();


# Saving the training and test data sets 

ap_train_data= datasets["application_train"]
ap_test_data = datasets["application_test"]


# Determining the categorical and numerical features


numerical_features = ap_train_data.select_dtypes(include = ['int64', 'float64']).columns
categorical_features = ap_train_data.select_dtypes(include = ['object', 'bool']).columns
print(f"\nNumerical features : {list(numerical_features)}")
print(f"\nCategorical features : {list(categorical_features)}")

Numerical features : ['SK_ID_CURR', 'TARGET', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE', 'REGION_POPULATION_RELATIVE', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'DAYS_REGISTRATION', 'DAYS_ID_PUBLISH', 'OWN_CAR_AGE', 'FLAG_MOBIL', 'FLAG_EMP_PHONE', 'FLAG_WORK_PHONE', 'FLAG_CONT_MOBILE', 'FLAG_PHONE', 'FLAG_EMAIL', 'CNT_FAM_MEMBERS', 'REGION_RATING_CLIENT', 'REGION_RATING_CLIENT_W_CITY', 'HOUR_APPR_PROCESS_START', 'REG_REGION_NOT_LIVE_REGION', 'REG_REGION_NOT_WORK_REGION', 'LIVE_REGION_NOT_WORK_REGION', 'REG_CITY_NOT_LIVE_CITY', 'REG_CITY_NOT_WORK_CITY', 'LIVE_CITY_NOT_WORK_CITY', 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'APARTMENTS_AVG', 'BASEMENTAREA_AVG', 'YEARS_BEGINEXPLUATATION_AVG', 'YEARS_BUILD_AVG', 'COMMONAREA_AVG', 'ELEVATORS_AVG', 'ENTRANCES_AVG', 'FLOORSMAX_AVG', 'FLOORSMIN_AVG', 'LANDAREA_AVG', 'LIVINGAPARTMENTS_AVG', 'LIVINGAREA_AVG', 'NONLIVINGAPARTMENTS_AVG', 'NONLIVINGAREA_AVG', 'APARTMENTS_MODE', 'BASEMENTAREA_MODE', 'YEARS_BEGINEXPLUATATION_MODE', 'YEARS_BUILD_MODE', 'COMMONAREA_MODE', 'ELEVATORS_MODE', 'ENTRANCES_MODE', 'FLOORSMAX_MODE', 'FLOORSMIN_MODE', 'LANDAREA_MODE', 'LIVINGAPARTMENTS_MODE', 'LIVINGAREA_MODE', 'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAREA_MODE', 'APARTMENTS_MEDI', 'BASEMENTAREA_MEDI', 'YEARS_BEGINEXPLUATATION_MEDI', 'YEARS_BUILD_MEDI', 'COMMONAREA_MEDI', 'ELEVATORS_MEDI', 'ENTRANCES_MEDI', 'FLOORSMAX_MEDI', 'FLOORSMIN_MEDI', 'LANDAREA_MEDI', 'LIVINGAPARTMENTS_MEDI', 'LIVINGAREA_MEDI', 'NONLIVINGAPARTMENTS_MEDI', 'NONLIVINGAREA_MEDI', 'TOTALAREA_MODE', 'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE', 'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE', 'DAYS_LAST_PHONE_CHANGE', 'FLAG_DOCUMENT_2', 'FLAG_DOCUMENT_3', 'FLAG_DOCUMENT_4', 'FLAG_DOCUMENT_5', 'FLAG_DOCUMENT_6', 'FLAG_DOCUMENT_7', 'FLAG_DOCUMENT_8', 'FLAG_DOCUMENT_9', 'FLAG_DOCUMENT_10', 'FLAG_DOCUMENT_11', 'FLAG_DOCUMENT_12', 'FLAG_DOCUMENT_13', 'FLAG_DOCUMENT_14', 'FLAG_DOCUMENT_15', 'FLAG_DOCUMENT_16', 'FLAG_DOCUMENT_17', 'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20', 'FLAG_DOCUMENT_21', 'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR']

Categorical features : ['NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'OCCUPATION_TYPE', 'WEEKDAY_APPR_PROCESS_START', 'ORGANIZATION_TYPE', 'FONDKAPREMONT_MODE', 'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE']


len(list(numerical_features))

106


len(list(categorical_features))

16


total_ip_features = len(list(numerical_features)) + len(list(categorical_features))
total_ip_features

122


#Distributin of target


import matplotlib.pyplot as plt
%matplotlib inline

datasets["application_train"]['TARGET'].astype(int).plot.hist();


ap_train_data['TARGET'].value_counts()

0    282686
1     24825
Name: TARGET, dtype: int64


plt.figure(figsize=(7, 7))
plt.pie(x=ap_train_data['TARGET'].value_counts(),
        radius=1.5-0.5,
        labels=ap_train_data['TARGET'].value_counts().index,
        autopct='%1.1f%%',
        colors=['fuchsia', 'yellow'],
        explode=[0,0.5],
        wedgeprops={"edgecolor":"0", "width":0.4},
        startangle=160,
        shadow=True,
        textprops={'fontsize': 14})
plt.ylabel('TARGET', fontsize=15)
plt.title('Plot for distribution of TARGET feature', fontsize=17)
plt.show()


correlations = datasets["application_train"].corr()['TARGET'].sort_values()
print('Most Positive Correlations:\n', correlations.tail(10))
print('\nMost Negative Correlations:\n', correlations.head(10))

Most Positive Correlations:
 FLAG_DOCUMENT_3                0.044346
REG_CITY_NOT_LIVE_CITY         0.044395
FLAG_EMP_PHONE                 0.045982
REG_CITY_NOT_WORK_CITY         0.050994
DAYS_ID_PUBLISH                0.051457
DAYS_LAST_PHONE_CHANGE         0.055218
REGION_RATING_CLIENT           0.058899
REGION_RATING_CLIENT_W_CITY    0.060893
DAYS_BIRTH                     0.078239
TARGET                         1.000000
Name: TARGET, dtype: float64

Most Negative Correlations:
 EXT_SOURCE_3                 -0.178919
EXT_SOURCE_2                 -0.160472
EXT_SOURCE_1                 -0.155317
DAYS_EMPLOYED                -0.044932
FLOORSMAX_AVG                -0.044003
FLOORSMAX_MEDI               -0.043768
FLOORSMAX_MODE               -0.043226
AMT_GOODS_PRICE              -0.039645
REGION_POPULATION_RELATIVE   -0.037227
ELEVATORS_AVG                -0.034199
Name: TARGET, dtype: float64


#creating the correlation for the training data
tr_data_corr=ap_train_data.corr()


plt.figure(figsize=(27, 17))
sns.heatmap(tr_data_corr, cmap='viridis')
plt.title("Correlation map for application traininig data")
plt.plot();


# Extracting the AMOUNT variables
#Representing the correlations
amt_data = tr_data_corr[['TARGET', 'AMT_CREDIT', 'AMT_INCOME_TOTAL', 'AMT_GOODS_PRICE']]
amt_corr = amt_data.corr()
print(amt_corr)

                    TARGET  AMT_CREDIT  AMT_INCOME_TOTAL  AMT_GOODS_PRICE
TARGET            1.000000   -0.143445         -0.079451        -0.153852
AMT_CREDIT       -0.143445    1.000000          0.355705         0.999502
AMT_INCOME_TOTAL -0.079451    0.355705          1.000000         0.358367
AMT_GOODS_PRICE  -0.153852    0.999502          0.358367         1.000000


amt_corr


plt.figure(figsize=(14, 8))
sns.heatmap(amt_corr, annot=True, cmap='PuRd')
plt.title("Heatmap for representing the AMOUNT correlations")
plt.plot();


ext_source_vars = ["EXT_SOURCE_3", "EXT_SOURCE_2", "EXT_SOURCE_1","DAYS_BIRTH"]
sns.pairplot(data = datasets["application_train"], hue="TARGET", vars = ext_source_vars, height=4, diag_kind="hist")
plt.title("Pair plots of the top 4 correlated features")
plt.show()


ext_source_vars_corr = tr_data_corr[ext_source_vars].corr()
ext_source_vars_corr


plt.figure(figsize=(12, 9))
sns.heatmap(ext_source_vars_corr, annot=True, cmap='PuRd')
plt.title("Correlation map of top 4 features")
plt.plot();


ext_source_vars_corr.isnull().count()


plt.hist(datasets["application_train"]['DAYS_BIRTH'] / -365, edgecolor = 'k', bins = 25)
plt.title('Age of Client'); plt.xlabel('Age (years)'); plt.ylabel('Count');


#Distribution based on gender,Applicants gender


gend = datasets["application_train"]['CODE_GENDER']
plt.figure(figsize=(10,7))
sns.countplot(data =datasets["application_train"], x= datasets["application_train"]['TARGET'], hue=gend, palette='rainbow')
plt.title("Distribution of target column based on Gender")
plt.show()


# creating different data frames for putting in age information
age_df = datasets["application_train"][['TARGET', 'DAYS_BIRTH']]
age_df['YEARS_BIRTH'] = age_df['DAYS_BIRTH'] / -365

# Bin the age data
age_df['GROUPED_YEARS_BIRTH'] = pd.cut(age_df['YEARS_BIRTH'], bins = np.linspace(20, 70, num = 11))
age_df.head(20)


age_group_years  = age_df.groupby('GROUPED_YEARS_BIRTH').mean()
age_group_years


age_group_years['YEARS_BIRTH'].plot.barh(figsize=(15, 7), color='crimson')
plt.xticks(fontsize=11)
plt.yticks(fontsize=11)
plt.xlabel('Frequency', fontsize=12)
plt.ylabel('Age Group', fontsize=12)
plt.grid(True)
plt.title('Age Group Distribution (Years)', fontsize=17)
plt.show()


plt.figure(figsize = (9, 9))

# Graph the age bins and the average of the target as a bar plot
plt.bar(age_group_years.index.astype(str), 100 * age_group_years['TARGET'],color='green')

# Label 
plt.xticks(rotation = 85); plt.xlabel('Age Group in Years'); plt.ylabel('Repayment Percentage Failure ')
plt.title('Repayment Failure based on Age Group');


sns.countplot(x='OCCUPATION_TYPE', data=datasets["application_train"]);
plt.title('Applicants Occupation');
plt.grid(True)
plt.xticks(rotation=90);


plt.figure(figsize=(20, 7))
sns.countplot(x='OCCUPATION_TYPE', data=datasets["application_train"], hue='TARGET', palette='rainbow')
plt.xlabel('Type Of Occupation', fontsize=15)
plt.ylabel('Count', fontsize=15)
plt.xticks(rotation=80, fontsize=13)
plt.yticks(fontsize=11)
plt.title('Occupation of Applicant', fontsize=17)
plt.grid(True)
plt.plot();


ap_train_data['NAME_EDUCATION_TYPE'].value_counts()

Secondary / secondary special    218391
Higher education                  74863
Incomplete higher                 10277
Lower secondary                    3816
Academic degree                     164
Name: NAME_EDUCATION_TYPE, dtype: int64


plt.figure(figsize=(15, 7))
sns.countplot(x='NAME_EDUCATION_TYPE', data=ap_train_data, palette='rainbow', hue='TARGET')
plt.xlabel('Type of Education', fontsize=15)
plt.ylabel('Count', fontsize=14)
plt.xticks(rotation=50, fontsize=13)
plt.yticks(fontsize=11)
plt.title("Applicant's Education Type", fontsize=15)
plt.grid(True)
plt.plot();


ap_train_data['NAME_HOUSING_TYPE'].value_counts()

House / apartment      272868
With parents            14840
Municipal apartment     11183
Rented apartment         4881
Office apartment         2617
Co-op apartment          1122
Name: NAME_HOUSING_TYPE, dtype: int64


plt.figure(figsize=(17, 7))
sns.countplot(x='NAME_HOUSING_TYPE', data=ap_train_data, palette='rainbow', hue='TARGET')
plt.xlabel('Type of Housing', fontsize=15)
plt.ylabel('Count', fontsize=14)
plt.xticks(rotation=50, fontsize=13)
plt.yticks(fontsize=11)
plt.title("Applicant's Housing Type", fontsize=15)
plt.grid(True)
plt.plot();


ap_train_data['NAME_CONTRACT_TYPE'].value_counts()

Cash loans         278232
Revolving loans     29279
Name: NAME_CONTRACT_TYPE, dtype: int64


plt.figure(figsize=(8, 8))
plt.pie(x=ap_train_data['NAME_CONTRACT_TYPE'].value_counts(),
        radius=1.5-0.5,
        labels=ap_train_data['NAME_CONTRACT_TYPE'].value_counts().index,
        autopct='%1.1f%%',
        colors=['fuchsia', 'yellow'],
        explode=[0,0.3],
        wedgeprops={"edgecolor":"0", "width":0.5},
        startangle=160,
        shadow=True,
        textprops={'fontsize': 15})
plt.ylabel('', fontsize=15)
plt.title("Applicant's Contract Type", fontsize=17)
plt.show()


plt.figure(figsize=(17, 30))
sns.countplot(y='ORGANIZATION_TYPE', data=ap_train_data,hue='TARGET',palette='rainbow')
plt.title("Type of Applicant's Organization", fontsize=15)
plt.xlabel('Organization Type', fontsize=15)
plt.ylabel('Count', fontsize=11)
plt.xticks(rotation=45, fontsize=12)
plt.yticks(fontsize=12)
plt.grid(True)
plt.plot();


ap_train_data['WALLSMATERIAL_MODE'].value_counts()

Panel           66040
Stone, brick    64815
Block            9253
Wooden           5362
Mixed            2296
Monolithic       1779
Others           1625
Name: WALLSMATERIAL_MODE, dtype: int64


plt.figure(figsize=(7, 7))
plt.pie(x=ap_train_data['WALLSMATERIAL_MODE'].value_counts(),
        radius=1.5-0.5,
        labels=ap_train_data['WALLSMATERIAL_MODE'].value_counts().index,
        autopct='%1.1f%%',
        colors=['fuchsia', 'yellow'],
        wedgeprops={"edgecolor":"0", "width":0.5},
        startangle=160,
        shadow=True,
        textprops={'fontsize': 15})
plt.ylabel('', fontsize=15)
plt.title("Applicant's House's Wall Material Type", fontsize=15)
plt.show()


ap_train_data['HOUSETYPE_MODE'].value_counts()

block of flats      150503
specific housing      1499
terraced house        1212
Name: HOUSETYPE_MODE, dtype: int64


plt.figure(figsize=(9, 9))
plt.pie(x=ap_train_data['HOUSETYPE_MODE'].value_counts(),
        radius=1.5-0.5,
        labels=ap_train_data['HOUSETYPE_MODE'].value_counts().index,
        autopct='%1.1f%%',
        colors=['fuchsia', 'yellow', 'green'],
        explode=[0,0.8,0.5],
        wedgeprops={"edgecolor":"0", "width":0.3},
        startangle=160,
        shadow=True,
        textprops={'fontsize': 14})
plt.ylabel('', fontsize=14)
plt.suptitle("Applicant's House Type", fontsize=16)
plt.show()


ap_train_data['FLAG_OWN_REALTY'].value_counts()

Y    213312
N     94199
Name: FLAG_OWN_REALTY, dtype: int64


#plotting if teh applicant already own the realty


plt.figure(figsize=(7, 7))
plt.pie(x=ap_train_data['FLAG_OWN_REALTY'].value_counts(),
        radius=1.5-0.5,
        labels=ap_train_data['FLAG_OWN_REALTY'].value_counts().index,
        autopct='%1.1f%%',
        colors=['fuchsia', 'yellow'],
        explode=[0,0.4],
        
        startangle=55,
        shadow=True,
        textprops={'fontsize': 15})
plt.ylabel('', fontsize=15)
plt.title("Does the applicant already own a Realty?", fontsize=15)
plt.show()


plt.figure(figsize=(17, 8))
sns.countplot(x='FLAG_OWN_REALTY', data=ap_train_data, palette='rainbow', hue='TARGET')

plt.xlabel('Owns Realty? (No Default | Default)', fontsize=15)
plt.ylabel('Count', fontsize=15)
plt.xticks(fontsize=11)
plt.yticks(fontsize=11)
plt.grid(True)
plt.title("If applicants owns realty versus Repayment", fontsize=15)
plt.plot();


plt.figure(figsize=(17, 7))
sns.countplot(x='CODE_GENDER', data=ap_train_data, palette='rainbow', hue='TARGET')

plt.xlabel('Gender (No Default | Default)', fontsize=15)
plt.ylabel('Count', fontsize=15)
plt.xticks(fontsize=11)
plt.yticks(fontsize=11)
plt.grid(True)
plt.title("Applicants' Gender and Repayment relation", fontsize=15)
plt.plot();


### **AMT_CREDIT feature distribution plotting**


plt.figure(figsize=(17, 7))
sns.violinplot(x=ap_train_data['AMT_CREDIT'], palette='rainbow')
plt.xticks(size=11)
plt.yticks(size=11)
plt.xlabel('AMT_CREDIT', size=15)
plt.title('Violinplot for AMT_CREDIT feature', size=15)
plt.grid(True)


## ** AMT_ANNUITY feature distribution plotting**


plt.figure(figsize=(17, 7))
sns.boxplot(x=ap_train_data['AMT_ANNUITY'], palette='rainbow')
plt.xticks(size=11)
plt.yticks(size=11)
plt.xlabel('AMT_ANNUITY', size=15)
plt.title('Boxplot for AMT_ANNUITY feature', size=15)
plt.grid(True)


# ** DAYS_EMPLOYED feature distribution plotting**


ap_train_data['DAYS_EMPLOYED'].describe()

count    307511.000000
mean      63815.045904
std      141275.766519
min      -17912.000000
25%       -2760.000000
50%       -1213.000000
75%        -289.000000
max      365243.000000
Name: DAYS_EMPLOYED, dtype: float64


plt.figure(figsize=(15, 7))
sns.distplot(x=ap_train_data['DAYS_EMPLOYED'], color='orange')
plt.xticks(size=12)
plt.yticks(size=12)
plt.xlabel('DAYS_EMPLOYED', size=14)
plt.title('KDE plot for DAYS_EMPLOYED feature', size=16)
plt.grid(True)


# **Distribution of DAYS_EMPLOYED feature after removing the inconsistent value**


days_employed_fixed = ap_train_data['DAYS_EMPLOYED']
days_employed_fixed = days_employed_fixed[days_employed_fixed<365243]
plt.figure(figsize=(17, 7))
sns.distplot(x=days_employed_fixed, color='blue')
plt.xticks(size=11)
plt.yticks(size=11)
plt.xlabel('DAYS_EMPLOYED', size=15)
plt.title('KDE plot for DAYS_EMPLOYED feature after fixing', size=15)
plt.grid(True)


# **Distribution of AMT_GOODS_PRICE feature**


plt.figure(figsize=(17, 7))
sns.distplot(x=ap_train_data['AMT_GOODS_PRICE'], color='red')
plt.xticks(size=11)
plt.yticks(size=11)
plt.xlabel('AMT_GOODS_PRICE', size=15)
plt.title('KDE plot for AMT_GOODS_PRICE feature', size=15)
plt.grid(True)


# Fixing the issues with DAYS_EMPLOYES and DAYS_BIRTH features


ap_train_data['DAYS_BIRTH'] = ap_train_data['DAYS_BIRTH'] / -1
ap_test_data['DAYS_BIRTH'] = ap_test_data['DAYS_BIRTH'] / -1

ap_train_data['DAYS_EMPLOYED'] = ap_train_data['DAYS_EMPLOYED'][ap_train_data['DAYS_EMPLOYED']<365243]
ap_test_data['DAYS_EMPLOYED'] = ap_test_data['DAYS_EMPLOYED'][ap_test_data['DAYS_EMPLOYED']<365243]
ap_train_data['DAYS_EMPLOYED'] = ap_train_data['DAYS_EMPLOYED']/-1
ap_test_data['DAYS_EMPLOYED'] = ap_test_data['DAYS_EMPLOYED']/-1


ap_train_data['DAYS_EMPLOYED'].head()

0     637.0
1    1188.0
2     225.0
3    3039.0
4    3038.0
Name: DAYS_EMPLOYED, dtype: float64


ap_test_data['DAYS_EMPLOYED'].head()

0    2329.0
1    4469.0
2    4458.0
3    1866.0
4    2191.0
Name: DAYS_EMPLOYED, dtype: float64


ap_train_data['DAYS_BIRTH'].head()

0     9461.0
1    16765.0
2    19046.0
3    19005.0
4    19932.0
Name: DAYS_BIRTH, dtype: float64


ap_test_data['DAYS_BIRTH'].head()

0    19241.0
1    18064.0
2    20038.0
3    13976.0
4    13040.0
Name: DAYS_BIRTH, dtype: float64


list(datasets.keys())

['application_train',
 'application_test',
 'bureau',
 'bureau_balance',
 'credit_card_balance',
 'installments_payments',
 'previous_application',
 'POS_CASH_balance']


len(datasets["application_train"]["SK_ID_CURR"].unique()) == datasets["application_train"].shape[0]

True


# is there an overlap between the test and train customers 
np.intersect1d(datasets["application_train"]["SK_ID_CURR"], datasets["application_test"]["SK_ID_CURR"])

array([], dtype=int64)


# 
datasets["application_test"].shape

(48744, 121)


datasets["application_train"].shape

(307511, 122)


appsDF = datasets["previous_application"]
display(appsDF.head())
print(f"{appsDF.shape[0]:,} rows, {appsDF.shape[1]:,} columns")

1,670,214 rows, 37 columns


print(f"There are  {appsDF.shape[0]:,} previous applications")

There are  1,670,214 previous applications


#Find the intersection of two arrays.
print(f'Number of train applicants with previous applications is {len(np.intersect1d(datasets["previous_application"]["SK_ID_CURR"], datasets["application_train"]["SK_ID_CURR"])):,}')

Number of train applicants with previous applications is 291,057


#Find the intersection of two arrays.
print(f'Number of train applicants with previous applications is {len(np.intersect1d(datasets["previous_application"]["SK_ID_CURR"], datasets["application_test"]["SK_ID_CURR"])):,}')

Number of train applicants with previous applications is 47,800


# How many previous applciations  per applicant in the previous_application 
prevAppCounts = appsDF['SK_ID_CURR'].value_counts(dropna=False)
len(prevAppCounts[prevAppCounts >40])  #more that 40 previous applications
plt.hist(prevAppCounts[prevAppCounts>=0], bins=100)
plt.grid()


prevAppCounts[prevAppCounts >50].plot(kind='bar')
plt.xticks(rotation=50)
plt.show()


sum(appsDF['SK_ID_CURR'].value_counts()==1)

60458


plt.hist(appsDF['SK_ID_CURR'].value_counts(), cumulative =True, bins = 100);
plt.grid()
plt.ylabel('cumulative number of IDs')
plt.xlabel('Number of previous applications per ID')
plt.title('Histogram of Number of previous applications for an ID')

Text(0.5, 1.0, 'Histogram of Number of previous applications for an ID')


apps_all = appsDF['SK_ID_CURR'].nunique()
apps_5plus = appsDF['SK_ID_CURR'].value_counts()>=5
apps_40plus = appsDF['SK_ID_CURR'].value_counts()>=40
print('Percentage with 10 or more previous apps:', np.round(100.*(sum(apps_5plus)/apps_all),5))
print('Percentage with 40 or more previous apps:', np.round(100.*(sum(apps_40plus)/apps_all),5))

Percentage with 10 or more previous apps: 41.76895
Percentage with 40 or more previous apps: 0.03453


plt.figure(figsize=(8, 8))
plt.pie(x=appsDF['NAME_CONTRACT_STATUS'].value_counts(),
        radius=1.3-0.3,
        labels=appsDF['NAME_CONTRACT_STATUS'].value_counts().index,
        autopct='%1.1f%%',
        colors=['crimson', 'yellow', 'blue', 'palegreen'],
        wedgeprops={"edgecolor":"0", "width":0.5},
        startangle=160,
        shadow=True,
        textprops={'fontsize': 15})
plt.ylabel('', fontsize=145)
plt.title("Applicant's Previous Contract Status ", fontsize=15)
plt.show()


plt.figure(figsize=(8, 8))
plt.pie(x=appsDF['NAME_CLIENT_TYPE'].value_counts(),
        radius=1.3-0.3,
        labels=appsDF['NAME_CLIENT_TYPE'].value_counts().index,
        autopct='%1.1f%%',
        colors=['red', 'blue', 'green', 'blue'],
        explode=[0.2,0,0.2,0],
        wedgeprops={"edgecolor":"0", "width":0.5},
        startangle=160,
        shadow=True,
        textprops={'fontsize': 15})
plt.ylabel('', fontsize=15)
plt.title("Type of Client in Previous Application", fontsize=15)
plt.show()


plt.figure(figsize=(14, 7))
sns.countplot(x='CHANNEL_TYPE', data=appsDF, palette='rainbow')
plt.xlabel('Channel Type', fontsize=15)
plt.ylabel('Count', fontsize=15)
plt.xticks(fontsize=11, rotation=55)
plt.yticks(fontsize=11)
plt.grid(True)
plt.title("Applicant's channel in previous application", fontsize=15)
plt.plot();


#Feature Engineering
#**Performing Encoding on Application train and Application Test's Categorical Features**


# Label Encoding
# Create a label encoder object
le = LabelEncoder()
le_count = 0

# Iterating through the columns
for col in ap_train_data:
    if ap_train_data[col].dtype == 'object':
        # If two or fewer unique categories
        if len(list(ap_train_data[col].unique())) <= 2:
            # Train on the training data
            le.fit(ap_train_data[col])
            # Transforming both training and testing data
            ap_train_data[col] = le.transform(ap_train_data[col])
            ap_test_data[col] = le.transform(ap_test_data[col])
            
            # Tracking of how many columns were label encoded
            le_count += 1
            
print('%d columns were label encoded.' % le_count)

3 columns were label encoded.


# one-hot encoding of features
ap_train_data = pd.get_dummies(ap_train_data)
ap_test_data = pd.get_dummies(ap_test_data)

print('Training Features shape: ', ap_train_data.shape)
print('Testing Features shape: ', ap_test_data.shape)

Training Features shape:  (307511, 243)
Testing Features shape:  (48744, 239)


training_labels = ap_train_data['TARGET']
# Aligning the training and testing data, keeping only columns present in both dataframes
ap_train_data, ap_test_data = ap_train_data.align(ap_test_data, join = 'inner', axis = 1)
# Add the target back in
ap_train_data['TARGET'] = training_labels
print('Training Features shape: ', ap_train_data.shape)
print('Testing Features shape: ', ap_test_data.shape)

Training Features shape:  (307511, 240)
Testing Features shape:  (48744, 239)


ap_train_data.to_csv('ap_train.csv', index=False)


ap_test_data.to_csv('ap_test.csv', index=False)


# Modeling and Baseline


#Numerical features used:


print(*list(numerical_features), sep = ",")

SK_ID_CURR,TARGET,CNT_CHILDREN,AMT_INCOME_TOTAL,AMT_CREDIT,AMT_ANNUITY,AMT_GOODS_PRICE,REGION_POPULATION_RELATIVE,DAYS_BIRTH,DAYS_EMPLOYED,DAYS_REGISTRATION,DAYS_ID_PUBLISH,OWN_CAR_AGE,FLAG_MOBIL,FLAG_EMP_PHONE,FLAG_WORK_PHONE,FLAG_CONT_MOBILE,FLAG_PHONE,FLAG_EMAIL,CNT_FAM_MEMBERS,REGION_RATING_CLIENT,REGION_RATING_CLIENT_W_CITY,HOUR_APPR_PROCESS_START,REG_REGION_NOT_LIVE_REGION,REG_REGION_NOT_WORK_REGION,LIVE_REGION_NOT_WORK_REGION,REG_CITY_NOT_LIVE_CITY,REG_CITY_NOT_WORK_CITY,LIVE_CITY_NOT_WORK_CITY,EXT_SOURCE_1,EXT_SOURCE_2,EXT_SOURCE_3,APARTMENTS_AVG,BASEMENTAREA_AVG,YEARS_BEGINEXPLUATATION_AVG,YEARS_BUILD_AVG,COMMONAREA_AVG,ELEVATORS_AVG,ENTRANCES_AVG,FLOORSMAX_AVG,FLOORSMIN_AVG,LANDAREA_AVG,LIVINGAPARTMENTS_AVG,LIVINGAREA_AVG,NONLIVINGAPARTMENTS_AVG,NONLIVINGAREA_AVG,APARTMENTS_MODE,BASEMENTAREA_MODE,YEARS_BEGINEXPLUATATION_MODE,YEARS_BUILD_MODE,COMMONAREA_MODE,ELEVATORS_MODE,ENTRANCES_MODE,FLOORSMAX_MODE,FLOORSMIN_MODE,LANDAREA_MODE,LIVINGAPARTMENTS_MODE,LIVINGAREA_MODE,NONLIVINGAPARTMENTS_MODE,NONLIVINGAREA_MODE,APARTMENTS_MEDI,BASEMENTAREA_MEDI,YEARS_BEGINEXPLUATATION_MEDI,YEARS_BUILD_MEDI,COMMONAREA_MEDI,ELEVATORS_MEDI,ENTRANCES_MEDI,FLOORSMAX_MEDI,FLOORSMIN_MEDI,LANDAREA_MEDI,LIVINGAPARTMENTS_MEDI,LIVINGAREA_MEDI,NONLIVINGAPARTMENTS_MEDI,NONLIVINGAREA_MEDI,TOTALAREA_MODE,OBS_30_CNT_SOCIAL_CIRCLE,DEF_30_CNT_SOCIAL_CIRCLE,OBS_60_CNT_SOCIAL_CIRCLE,DEF_60_CNT_SOCIAL_CIRCLE,DAYS_LAST_PHONE_CHANGE,FLAG_DOCUMENT_2,FLAG_DOCUMENT_3,FLAG_DOCUMENT_4,FLAG_DOCUMENT_5,FLAG_DOCUMENT_6,FLAG_DOCUMENT_7,FLAG_DOCUMENT_8,FLAG_DOCUMENT_9,FLAG_DOCUMENT_10,FLAG_DOCUMENT_11,FLAG_DOCUMENT_12,FLAG_DOCUMENT_13,FLAG_DOCUMENT_14,FLAG_DOCUMENT_15,FLAG_DOCUMENT_16,FLAG_DOCUMENT_17,FLAG_DOCUMENT_18,FLAG_DOCUMENT_19,FLAG_DOCUMENT_20,FLAG_DOCUMENT_21,AMT_REQ_CREDIT_BUREAU_HOUR,AMT_REQ_CREDIT_BUREAU_DAY,AMT_REQ_CREDIT_BUREAU_WEEK,AMT_REQ_CREDIT_BUREAU_MON,AMT_REQ_CREDIT_BUREAU_QRT,AMT_REQ_CREDIT_BUREAU_YEAR


# Categorical Features used:


print(*list(categorical_features), sep = ",")

NAME_CONTRACT_TYPE,CODE_GENDER,FLAG_OWN_CAR,FLAG_OWN_REALTY,NAME_TYPE_SUITE,NAME_INCOME_TYPE,NAME_EDUCATION_TYPE,NAME_FAMILY_STATUS,NAME_HOUSING_TYPE,OCCUPATION_TYPE,WEEKDAY_APPR_PROCESS_START,ORGANIZATION_TYPE,FONDKAPREMONT_MODE,HOUSETYPE_MODE,WALLSMATERIAL_MODE,EMERGENCYSTATE_MODE


df = pd.DataFrame([[1, 2, 3],
                    [4, 5, 6],
                   [7, 8, 9],
                   [np.nan, np.nan, np.nan]],
                   columns=['A', 'B', 'C'])
display(df)


df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
#        A    B
#max   NaN  8.0
#min   1.0  2.0
#sum  12.0  NaN


df = pd.DataFrame({'A': [1, 1, 2, 2],
                    'B': [1, 2, 3, 4],
                    'C': np.random.randn(4)})
display(df)


# group by column A: 
df.groupby('A').agg({'B': ['min', 'max'], 'C': 'sum'})
#    B             C
#  min max       sum
#A
#1   1   2  0.590716
#2   3   4  0.704907


appsDF.columns

Index(['SK_ID_PREV', 'SK_ID_CURR', 'NAME_CONTRACT_TYPE', 'AMT_ANNUITY',
       'AMT_APPLICATION', 'AMT_CREDIT', 'AMT_DOWN_PAYMENT', 'AMT_GOODS_PRICE',
       'WEEKDAY_APPR_PROCESS_START', 'HOUR_APPR_PROCESS_START',
       'FLAG_LAST_APPL_PER_CONTRACT', 'NFLAG_LAST_APPL_IN_DAY',
       'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',
       'RATE_INTEREST_PRIVILEGED', 'NAME_CASH_LOAN_PURPOSE',
       'NAME_CONTRACT_STATUS', 'DAYS_DECISION', 'NAME_PAYMENT_TYPE',
       'CODE_REJECT_REASON', 'NAME_TYPE_SUITE', 'NAME_CLIENT_TYPE',
       'NAME_GOODS_CATEGORY', 'NAME_PORTFOLIO', 'NAME_PRODUCT_TYPE',
       'CHANNEL_TYPE', 'SELLERPLACE_AREA', 'NAME_SELLER_INDUSTRY',
       'CNT_PAYMENT', 'NAME_YIELD_GROUP', 'PRODUCT_COMBINATION',
       'DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE', 'DAYS_LAST_DUE_1ST_VERSION',
       'DAYS_LAST_DUE', 'DAYS_TERMINATION', 'NFLAG_INSURED_ON_APPROVAL'],
      dtype='object')


funcs = ["a","b","c"]
{f:f"{f}_max" for f in funcs}

{'a': 'a_max', 'b': 'b_max', 'c': 'c_max'}


appsDF[0:50][(appsDF["SK_ID_CURR"]==175704)]


appsDF[0:50][(appsDF["SK_ID_CURR"]==175704)]["AMT_CREDIT"]

6    0.0
Name: AMT_CREDIT, dtype: float64


appsDF[0:50][(appsDF["SK_ID_CURR"]==175704) & ~(appsDF["AMT_CREDIT"]==1.0)]


appsDF.isna().sum()

SK_ID_PREV                           0
SK_ID_CURR                           0
NAME_CONTRACT_TYPE                   0
AMT_ANNUITY                     372235
AMT_APPLICATION                      0
AMT_CREDIT                           1
AMT_DOWN_PAYMENT                895844
AMT_GOODS_PRICE                 385515
WEEKDAY_APPR_PROCESS_START           0
HOUR_APPR_PROCESS_START              0
FLAG_LAST_APPL_PER_CONTRACT          0
NFLAG_LAST_APPL_IN_DAY               0
RATE_DOWN_PAYMENT               895844
RATE_INTEREST_PRIMARY          1664263
RATE_INTEREST_PRIVILEGED       1664263
NAME_CASH_LOAN_PURPOSE               0
NAME_CONTRACT_STATUS                 0
DAYS_DECISION                        0
NAME_PAYMENT_TYPE                    0
CODE_REJECT_REASON                   0
NAME_TYPE_SUITE                 820405
NAME_CLIENT_TYPE                     0
NAME_GOODS_CATEGORY                  0
NAME_PORTFOLIO                       0
NAME_PRODUCT_TYPE                    0
CHANNEL_TYPE                         0
SELLERPLACE_AREA                     0
NAME_SELLER_INDUSTRY                 0
CNT_PAYMENT                     372230
NAME_YIELD_GROUP                     0
PRODUCT_COMBINATION                346
DAYS_FIRST_DRAWING              673065
DAYS_FIRST_DUE                  673065
DAYS_LAST_DUE_1ST_VERSION       673065
DAYS_LAST_DUE                   673065
DAYS_TERMINATION                673065
NFLAG_INSURED_ON_APPROVAL       673065
dtype: int64


appsDF.columns

Index(['SK_ID_PREV', 'SK_ID_CURR', 'NAME_CONTRACT_TYPE', 'AMT_ANNUITY',
       'AMT_APPLICATION', 'AMT_CREDIT', 'AMT_DOWN_PAYMENT', 'AMT_GOODS_PRICE',
       'WEEKDAY_APPR_PROCESS_START', 'HOUR_APPR_PROCESS_START',
       'FLAG_LAST_APPL_PER_CONTRACT', 'NFLAG_LAST_APPL_IN_DAY',
       'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',
       'RATE_INTEREST_PRIVILEGED', 'NAME_CASH_LOAN_PURPOSE',
       'NAME_CONTRACT_STATUS', 'DAYS_DECISION', 'NAME_PAYMENT_TYPE',
       'CODE_REJECT_REASON', 'NAME_TYPE_SUITE', 'NAME_CLIENT_TYPE',
       'NAME_GOODS_CATEGORY', 'NAME_PORTFOLIO', 'NAME_PRODUCT_TYPE',
       'CHANNEL_TYPE', 'SELLERPLACE_AREA', 'NAME_SELLER_INDUSTRY',
       'CNT_PAYMENT', 'NAME_YIELD_GROUP', 'PRODUCT_COMBINATION',
       'DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE', 'DAYS_LAST_DUE_1ST_VERSION',
       'DAYS_LAST_DUE', 'DAYS_TERMINATION', 'NFLAG_INSURED_ON_APPROVAL'],
      dtype='object')


# appsDF[agg_op_features].head()


agg_ops

['min', 'max', 'mean']


features = ['AMT_ANNUITY', 'AMT_APPLICATION']
print(f"{appsDF[features].describe()}")
agg_ops = ["min", "max", "mean"]
result = appsDF.groupby(["SK_ID_CURR"], as_index=False).agg("mean") #group by ID
display(result.head())
print("-"*50)
result = appsDF.groupby(["SK_ID_CURR"], as_index=False).agg({'AMT_ANNUITY' : agg_ops, 'AMT_APPLICATION' : agg_ops})
result.columns = result.columns.map('_'.join)
display(result)
result['range_AMT_APPLICATION'] = result['AMT_APPLICATION_max'] - result['AMT_APPLICATION_min']
print(f"result.shape: {result.shape}")
result[0:10]

        AMT_ANNUITY  AMT_APPLICATION
count  1.297979e+06     1.670214e+06
mean   1.595512e+04     1.752339e+05
std    1.478214e+04     2.927798e+05
min    0.000000e+00     0.000000e+00
25%    6.321780e+03     1.872000e+04
50%    1.125000e+04     7.104600e+04
75%    2.065842e+04     1.803600e+05
max    4.180581e+05     6.905160e+06

--------------------------------------------------

result.shape: (338857, 8)


result.isna().sum()

SK_ID_CURR_                0
AMT_ANNUITY_min          480
AMT_ANNUITY_max          480
AMT_ANNUITY_mean         480
AMT_APPLICATION_min        0
AMT_APPLICATION_max        0
AMT_APPLICATION_mean       0
range_AMT_APPLICATION      0
dtype: int64


# # Create aggregate features (via pipeline)
# class prevAppsFeaturesAggregater(BaseEstimator, TransformerMixin):
#     def __init__(self, features=None): # no *args or **kargs
#         self.features = features
#         self.agg_op_features = {}
#         for f in features:
#             self.agg_op_features[f] = {f"{f}_{func}":func for func in ["min", "max", "mean"]}

#     def fit(self, X, y=None):
#         return self
    
#     def transform(self, X, y=None):
#         #from IPython.core.debugger import Pdb as pdb;    pdb().set_trace() #breakpoint; dont forget to quit         
#         result = X.groupby(["SK_ID_CURR"]).agg(self.agg_op_features)
#         result.columns = result.columns.droplevel()
#         result = result.reset_index(level=["SK_ID_CURR"])
#         result['range_AMT_APPLICATION'] = result['AMT_APPLICATION_max'] - result['AMT_APPLICATION_min']
#         return result # return dataframe with the join key "SK_ID_CURR"
    

# from sklearn.pipeline import make_pipeline 
# def test_driver_prevAppsFeaturesAggregater(df, features):
#     print(f"df.shape: {df.shape}\n")
#     print(f"df[{features}][0:5]: \n{df[features][0:5]}")
#     test_pipeline = make_pipeline(prevAppsFeaturesAggregater(features))
#     return(test_pipeline.fit_transform(df))
         
# features = ['AMT_ANNUITY', 'AMT_APPLICATION']
# features = ['AMT_ANNUITY',
#        'AMT_APPLICATION', 'AMT_CREDIT', 'AMT_DOWN_PAYMENT', 'AMT_GOODS_PRICE',
#        'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',
#        'RATE_INTEREST_PRIVILEGED', 'DAYS_DECISION', 'NAME_PAYMENT_TYPE',
#        'CNT_PAYMENT', 
#        'DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE', 'DAYS_LAST_DUE_1ST_VERSION',
#        'DAYS_LAST_DUE', 'DAYS_TERMINATION']
# features = ['AMT_ANNUITY', 'AMT_APPLICATION']
# res = test_driver_prevAppsFeaturesAggregater(appsDF, features)
# print(f"HELLO")
# print(f"Test driver: \n{res[0:10]}")
# print(f"input[features][0:10]: \n{appsDF[0:10]}")


# # QUESTION, should we lower case df['OCCUPATION_TYPE'] as Sales staff != 'Sales Staff'? (hint: YES)


from sklearn.preprocessing import PolynomialFeatures
from sklearn.impute import SimpleImputer

# Make a new dataframe for polynomial features
poly_features_df = ap_train_data[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'DAYS_BIRTH', 'TARGET']]
poly_features_test_df = ap_test_data[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'DAYS_BIRTH']]

# imputer for handling missing values
# from sklearn.preprocessing import Imputer
imputer = SimpleImputer(strategy = 'median')

pn_target = poly_features_df['TARGET']

poly_features_df = poly_features_df.drop(columns = ['TARGET'])

# Need to impute missing values
poly_features_df = imputer.fit_transform(poly_features_df)
poly_features_test_df = imputer.transform(poly_features_test_df)
                                  
# Create the polynomial object with specified degree
pn_transformer = PolynomialFeatures(degree = 3)


# Train the polynomial features
pn_transformer.fit(poly_features_df)

# Transform the features
poly_features_df = pn_transformer.transform(poly_features_df)
poly_features_test_df = pn_transformer.transform(poly_features_test_df)
print('Shape of polynomial features: ', poly_features_df.shape)

Shape of polynomial features:  (307511, 35)


pn_transformer.get_feature_names(input_features = ['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'DAYS_BIRTH', 'DAYS_EMPLOYED'])[:20]

['1',
 'EXT_SOURCE_1',
 'EXT_SOURCE_2',
 'EXT_SOURCE_3',
 'DAYS_BIRTH',
 'EXT_SOURCE_1^2',
 'EXT_SOURCE_1 EXT_SOURCE_2',
 'EXT_SOURCE_1 EXT_SOURCE_3',
 'EXT_SOURCE_1 DAYS_BIRTH',
 'EXT_SOURCE_2^2',
 'EXT_SOURCE_2 EXT_SOURCE_3',
 'EXT_SOURCE_2 DAYS_BIRTH',
 'EXT_SOURCE_3^2',
 'EXT_SOURCE_3 DAYS_BIRTH',
 'DAYS_BIRTH^2',
 'EXT_SOURCE_1^3',
 'EXT_SOURCE_1^2 EXT_SOURCE_2',
 'EXT_SOURCE_1^2 EXT_SOURCE_3',
 'EXT_SOURCE_1^2 DAYS_BIRTH',
 'EXT_SOURCE_1 EXT_SOURCE_2^2']


# Create a dataframe of the features 
poly_features_df = pd.DataFrame(poly_features_df, 
                             columns = pn_transformer.get_feature_names(['EXT_SOURCE_1', 'EXT_SOURCE_2', 
                                                                           'EXT_SOURCE_3', 'DAYS_BIRTH']))

# Add in the target
poly_features_df['TARGET'] = pn_target

# Find the correlations with the target
pn_corrs = poly_features_df.corr()['TARGET'].sort_values()

# Display most negative and most positive
print(pn_corrs.head(10))
print(pn_corrs.tail(5))

EXT_SOURCE_2 EXT_SOURCE_3                -0.193939
EXT_SOURCE_1 EXT_SOURCE_2 EXT_SOURCE_3   -0.189605
EXT_SOURCE_2 EXT_SOURCE_3 DAYS_BIRTH     -0.181283
EXT_SOURCE_2^2 EXT_SOURCE_3              -0.176428
EXT_SOURCE_2 EXT_SOURCE_3^2              -0.172282
EXT_SOURCE_1 EXT_SOURCE_2                -0.166625
EXT_SOURCE_1 EXT_SOURCE_3                -0.164065
EXT_SOURCE_2                             -0.160295
EXT_SOURCE_2 DAYS_BIRTH                  -0.156873
EXT_SOURCE_1 EXT_SOURCE_2^2              -0.156867
Name: TARGET, dtype: float64
DAYS_BIRTH     -0.078239
DAYS_BIRTH^2   -0.076672
DAYS_BIRTH^3   -0.074273
TARGET          1.000000
1                    NaN
Name: TARGET, dtype: float64


# copying test features into dataframe
poly_features_test_df = pd.DataFrame(poly_features_test_df, 
                                  columns = pn_transformer.get_feature_names(['EXT_SOURCE_1', 'EXT_SOURCE_2', 
                                                                                'EXT_SOURCE_3', 'DAYS_BIRTH',
                                                                                'DAYS_EMPLOYED']))

# Merge polynomial features into training dataframe
poly_features_df['SK_ID_CURR'] = ap_train_data['SK_ID_CURR']
ap_train_data_poly = ap_train_data.merge(poly_features_df, on = 'SK_ID_CURR', how = 'left')

# Merge polnomial features into testing dataframe
poly_features_test_df['SK_ID_CURR'] = ap_test_data['SK_ID_CURR']
ap_test_data_poly = ap_test_data.merge(poly_features_test_df, on = 'SK_ID_CURR', how = 'left')

# Align the dataframes
ap_train_data_poly, ap_test_data_poly = ap_train_data_poly.align(ap_test_data_poly, join = 'inner', axis = 1)

# Print out the new shapes
print('Training data with polynomial features shape: ', ap_train_data_poly.shape)
print('Testing data with polynomial features shape:  ', ap_test_data_poly.shape)

Training data with polynomial features shape:  (307511, 274)
Testing data with polynomial features shape:   (48744, 274)


ap_train_data_poly['TARGET'] = ap_train_data['TARGET']
ap_train_data_poly.head()


ap_train_data_poly['TARGET'].head()

0    1
1    0
2    0
3    0
4    0
Name: TARGET, dtype: int64


 ap_test_data_poly.head()


~3==3

False


datasets.keys()

dict_keys(['application_train', 'application_test', 'bureau', 'bureau_balance', 'credit_card_balance', 'installments_payments', 'previous_application', 'POS_CASH_balance'])


# features = ['AMT_ANNUITY', 'AMT_APPLICATION']
# prevApps_feature_pipeline = Pipeline([
#         ('prevApps_add_features1', prevApps_add_features1()),  # add some new features 
#         ('prevApps_add_features2', prevApps_add_features2()),  # add some new features
#         ('prevApps_aggregater', prevAppsFeaturesAggregater()), # Aggregate across old and new features
#     ])


#X_train= datasets["application_train"] #primary dataset

merged_data_df=ap_train_data_poly#primary dataset
appsDF = datasets["previous_application"] #prev app


merge_all_data = False

# transform all the secondary tables
# 'bureau', 'bureau_balance', 'credit_card_balance', 'installments_payments', 
# 'previous_application', 'POS_CASH_balance'

if merge_all_data:
    prevApps_aggregated = prevApps_feature_pipeline.transform(appsDF)
    
    #'bureau', 'bureau_balance', 'credit_card_balance', 'installments_payments', 
    # 'previous_application', 'POS_CASH_balance'

# merge primary table and secondary tables using features based on meta data and  aggregage stats 
if merge_all_data:
    # 1. Join/Merge in prevApps Data
    X_train = X_train.merge(prevApps_aggregated, how='left', on='SK_ID_CURR')

    # 2. Join/Merge in ...... Data
    #X_train = X_train.merge(...._aggregated, how='left', on="SK_ID_CURR")

    # 3. Join/Merge in .....Data
    #dX_train = X_train.merge(...._aggregated, how='left', on="SK_ID_CURR")

    # 4. Join/Merge in Aggregated ...... Data
    #X_train = X_train.merge(...._aggregated, how='left', on="SK_ID_CURR")

    # .....
    
merged_data_df.head(10)


# X_kaggle_test= datasets["application_test"]
# if merge_all_data:
#     # 1. Join/Merge in prevApps Data
#     X_kaggle_test = X_kaggle_test.merge(prevApps_aggregated, how='left', on='SK_ID_CURR')

#     # 2. Join/Merge in ...... Data
#     #X_train = X_train.merge(...._aggregated, how='left', on="SK_ID_CURR")

#     # 3. Join/Merge in .....Data
#     #df_labeled = df_labeled.merge(...._aggregated, how='left', on="SK_ID_CURR")

#     # 4. Join/Merge in Aggregated ...... Data
#     #df_labeled = df_labeled.merge(...._aggregated, how='left', on="SK_ID_CURR")

#     # ......


# approval rate 'NFLAG_INSURED_ON_APPROVAL'


ap_train_data_poly.to_csv('app_train_pn.csv', index=False)
ap_test_data_poly.to_csv('app_test_pn.csv', index=False)


# Convert categorical features to numerical approximations (via pipeline)
class ClaimAttributesAdder(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        return self
    def transform(self, X, y=None): 
        charlson_idx_dt = {'0': 0, '1-2': 2, '3-4': 4, '5+': 6}
        los_dt = {'1 day': 1, '2 days': 2, '3 days': 3, '4 days': 4, '5 days': 5, '6 days': 6,
          '1- 2 weeks': 11, '2- 4 weeks': 21, '4- 8 weeks': 42, '26+ weeks': 180}
        X['PayDelay'] = X['PayDelay'].apply(lambda x: int(x) if x != '162+' else int(162))
        X['DSFS'] = X['DSFS'].apply(lambda x: None if pd.isnull(x) else int(x[0]) + 1)
        X['CharlsonIndex'] = X['CharlsonIndex'].apply(lambda x: charlson_idx_dt[x])
        X['LengthOfStay'] = X['LengthOfStay'].apply(lambda x: None if pd.isnull(x) else los_dt[x])
        return X


# Create a class to select numerical or categorical columns 
# since Scikit-Learn doesn't handle DataFrames yet
class DataFrameSelector(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names = attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names].values


# Identify the numeric features we wish to consider. 
num_attribs =[
'AMT_INCOME_TOTAL',
'AMT_CREDIT',
'EXT_SOURCE_3_x',
'EXT_SOURCE_2_x',
'EXT_SOURCE_1_x',
'EXT_SOURCE_3_y',
'EXT_SOURCE_2_y',
'EXT_SOURCE_1_y',
'DAYS_EMPLOYED',
'FLOORSMAX_AVG',
'FLOORSMAX_MEDI',
'FLOORSMAX_MODE',
'AMT_GOODS_PRICE',
'REGION_POPULATION_RELATIVE',
'ELEVATORS_AVG',
'REG_CITY_NOT_LIVE_CITY',
'FLAG_EMP_PHONE',
'REG_CITY_NOT_WORK_CITY',
'DAYS_ID_PUBLISH',
'DAYS_LAST_PHONE_CHANGE',
'REGION_RATING_CLIENT',
'REGION_RATING_CLIENT_W_CITY',
'AMT_REQ_CREDIT_BUREAU_HOUR',
'AMT_REQ_CREDIT_BUREAU_DAY',
'AMT_REQ_CREDIT_BUREAU_WEEK',
'AMT_REQ_CREDIT_BUREAU_MON',
'AMT_REQ_CREDIT_BUREAU_QRT',
'AMT_REQ_CREDIT_BUREAU_YEAR',
'1',
'EXT_SOURCE_1_y',
'EXT_SOURCE_2_y',
'EXT_SOURCE_3_y',
'DAYS_BIRTH_y',
'EXT_SOURCE_1^2',
'EXT_SOURCE_1 EXT_SOURCE_2',
'EXT_SOURCE_1 EXT_SOURCE_3',
'EXT_SOURCE_1 DAYS_BIRTH',
'EXT_SOURCE_2^2',
'EXT_SOURCE_2 EXT_SOURCE_3',
'EXT_SOURCE_2 DAYS_BIRTH',
'EXT_SOURCE_3^2',
'EXT_SOURCE_3 DAYS_BIRTH',
'DAYS_BIRTH^2',
'EXT_SOURCE_1^3',
'EXT_SOURCE_1^2 EXT_SOURCE_2',
'EXT_SOURCE_1^2 EXT_SOURCE_3',
'EXT_SOURCE_1^2 DAYS_BIRTH',
'EXT_SOURCE_1 EXT_SOURCE_2^2',
'EXT_SOURCE_1 EXT_SOURCE_2 EXT_SOURCE_3',
'EXT_SOURCE_1 EXT_SOURCE_2 DAYS_BIRTH',
'EXT_SOURCE_1 EXT_SOURCE_3^2',
'EXT_SOURCE_1 EXT_SOURCE_3 DAYS_BIRTH',
'EXT_SOURCE_1 DAYS_BIRTH^2',
'EXT_SOURCE_2^3',
'EXT_SOURCE_2^2 EXT_SOURCE_3',
'EXT_SOURCE_2^2 DAYS_BIRTH',
'EXT_SOURCE_2 EXT_SOURCE_3^2',
'EXT_SOURCE_2 EXT_SOURCE_3 DAYS_BIRTH',
'EXT_SOURCE_2 DAYS_BIRTH^2',
'EXT_SOURCE_3^3',
'EXT_SOURCE_3^2 DAYS_BIRTH',
'EXT_SOURCE_3 DAYS_BIRTH^2',
'DAYS_BIRTH^3']

num_pipeline = Pipeline([
        ('selector', DataFrameSelector(num_attribs)),
        ('imputer', SimpleImputer(strategy='median')),
        ('std_scaler', StandardScaler()),
    ])
# Identify the categorical features we wish to consider.
cat_attribs = ['FLAG_OWN_REALTY','FLAG_OWN_CAR','NAME_CONTRACT_TYPE', 'FLAG_OWN_CAR']

# Notice handle_unknown="ignore" in OHE which ignore values from the validation/test that
# do NOT occur in the training set
cat_pipeline = Pipeline([
        ('selector', DataFrameSelector(cat_attribs)),
        ('imputer', SimpleImputer(strategy='most_frequent')),
        # ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
        ('ohe', OneHotEncoder(sparse=False, handle_unknown="ignore"))
    ])

data_prep_pipeline = FeatureUnion(transformer_list=[
        ("num_pipeline", num_pipeline),
        ("cat_pipeline", cat_pipeline),
    ])
              
selected_features = numerical_attris + categorical_attris
tot_features = f"{len(selected_features)}:   Num:{len(numerical_attris)},    Cat:{len(categorical_attris)}"
tot_features

'67:   Num:63,    Cat:4'


# # Split the provided training data into training and validationa and test
# # The kaggle evaluation test set has no labels
# #
# from sklearn.model_selection import train_test_split

# use_application_data_ONLY = False #use joined data
# if use_application_data_ONLY:
#     # just selected a few features for a baseline experiment
#     selected_features = ['AMT_INCOME_TOTAL',  'AMT_CREDIT','DAYS_EMPLOYED','DAYS_BIRTH','EXT_SOURCE_1',
#         'EXT_SOURCE_2','EXT_SOURCE_3','CODE_GENDER', 'FLAG_OWN_REALTY','FLAG_OWN_CAR','NAME_CONTRACT_TYPE', 
#                    'NAME_EDUCATION_TYPE','OCCUPATION_TYPE','NAME_INCOME_TYPE']
#     X_train = datasets["application_train"][selected_features]
#     y_train = datasets["application_train"]['TARGET']
#     X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.15, random_state=42)
#     X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.15, random_state=42)
#     X_kaggle_test= datasets["application_test"][selected_features]
#     # y_test = datasets["application_test"]['TARGET']   #why no  TARGET?!! (hint: kaggle competition)

# selected_features = ['AMT_INCOME_TOTAL',  'AMT_CREDIT','DAYS_EMPLOYED','DAYS_BIRTH','EXT_SOURCE_1',
#         'EXT_SOURCE_2','EXT_SOURCE_3','CODE_GENDER', 'FLAG_OWN_REALTY','FLAG_OWN_CAR','NAME_CONTRACT_TYPE', 
#                    'NAME_EDUCATION_TYPE','OCCUPATION_TYPE','NAME_INCOME_TYPE']
# y_train = X_train['TARGET']
# X_train = X_train[selected_features]
# X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.15, random_state=42)
# X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.15, random_state=42)
# X_kaggle_test= X_kaggle_test[selected_features]
# # y_test = datasets["application_test"]['TARGET']   #why no  TARGET?!! (hint: kaggle competition)

    
# print(f"X train           shape: {X_train.shape}")
# print(f"X validation      shape: {X_valid.shape}")
# print(f"X test            shape: {X_test.shape}")
# print(f"X X_kaggle_test   shape: {X_kaggle_test.shape}")


# # Split the provided training data into training and validationa and test
# # The kaggle evaluation test set has no labels

X_train = training_dataset[selected_features]
y_train = ap_train_data_poly["TARGET"]
X_kaggle_test = ap_test_data_poly[selected_features]

subsample_rat = 0.3

X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, stratify=y_train,
                                                    test_size=subsample_rat, random_state=42)

X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train,stratify=y_train,
                                                      test_size=0.15, random_state=42)

print(f"X train           shape: {X_train.shape}")
print(f"X validation      shape: {X_valid.shape}")
print(f"X test            shape: {X_test.shape}")
print(f"X X_kaggle_test   shape: {X_kaggle_test.shape}")

X train           shape: (182968, 67)
X validation      shape: (32289, 67)
X test            shape: (92254, 67)
X X_kaggle_test   shape: (48744, 67)


list(datasets["application_train"].columns)

['SK_ID_CURR',
 'TARGET',
 'NAME_CONTRACT_TYPE',
 'CODE_GENDER',
 'FLAG_OWN_CAR',
 'FLAG_OWN_REALTY',
 'CNT_CHILDREN',
 'AMT_INCOME_TOTAL',
 'AMT_CREDIT',
 'AMT_ANNUITY',
 'AMT_GOODS_PRICE',
 'NAME_TYPE_SUITE',
 'NAME_INCOME_TYPE',
 'NAME_EDUCATION_TYPE',
 'NAME_FAMILY_STATUS',
 'NAME_HOUSING_TYPE',
 'REGION_POPULATION_RELATIVE',
 'DAYS_BIRTH',
 'DAYS_EMPLOYED',
 'DAYS_REGISTRATION',
 'DAYS_ID_PUBLISH',
 'OWN_CAR_AGE',
 'FLAG_MOBIL',
 'FLAG_EMP_PHONE',
 'FLAG_WORK_PHONE',
 'FLAG_CONT_MOBILE',
 'FLAG_PHONE',
 'FLAG_EMAIL',
 'OCCUPATION_TYPE',
 'CNT_FAM_MEMBERS',
 'REGION_RATING_CLIENT',
 'REGION_RATING_CLIENT_W_CITY',
 'WEEKDAY_APPR_PROCESS_START',
 'HOUR_APPR_PROCESS_START',
 'REG_REGION_NOT_LIVE_REGION',
 'REG_REGION_NOT_WORK_REGION',
 'LIVE_REGION_NOT_WORK_REGION',
 'REG_CITY_NOT_LIVE_CITY',
 'REG_CITY_NOT_WORK_CITY',
 'LIVE_CITY_NOT_WORK_CITY',
 'ORGANIZATION_TYPE',
 'EXT_SOURCE_1',
 'EXT_SOURCE_2',
 'EXT_SOURCE_3',
 'APARTMENTS_AVG',
 'BASEMENTAREA_AVG',
 'YEARS_BEGINEXPLUATATION_AVG',
 'YEARS_BUILD_AVG',
 'COMMONAREA_AVG',
 'ELEVATORS_AVG',
 'ENTRANCES_AVG',
 'FLOORSMAX_AVG',
 'FLOORSMIN_AVG',
 'LANDAREA_AVG',
 'LIVINGAPARTMENTS_AVG',
 'LIVINGAREA_AVG',
 'NONLIVINGAPARTMENTS_AVG',
 'NONLIVINGAREA_AVG',
 'APARTMENTS_MODE',
 'BASEMENTAREA_MODE',
 'YEARS_BEGINEXPLUATATION_MODE',
 'YEARS_BUILD_MODE',
 'COMMONAREA_MODE',
 'ELEVATORS_MODE',
 'ENTRANCES_MODE',
 'FLOORSMAX_MODE',
 'FLOORSMIN_MODE',
 'LANDAREA_MODE',
 'LIVINGAPARTMENTS_MODE',
 'LIVINGAREA_MODE',
 'NONLIVINGAPARTMENTS_MODE',
 'NONLIVINGAREA_MODE',
 'APARTMENTS_MEDI',
 'BASEMENTAREA_MEDI',
 'YEARS_BEGINEXPLUATATION_MEDI',
 'YEARS_BUILD_MEDI',
 'COMMONAREA_MEDI',
 'ELEVATORS_MEDI',
 'ENTRANCES_MEDI',
 'FLOORSMAX_MEDI',
 'FLOORSMIN_MEDI',
 'LANDAREA_MEDI',
 'LIVINGAPARTMENTS_MEDI',
 'LIVINGAREA_MEDI',
 'NONLIVINGAPARTMENTS_MEDI',
 'NONLIVINGAREA_MEDI',
 'FONDKAPREMONT_MODE',
 'HOUSETYPE_MODE',
 'TOTALAREA_MODE',
 'WALLSMATERIAL_MODE',
 'EMERGENCYSTATE_MODE',
 'OBS_30_CNT_SOCIAL_CIRCLE',
 'DEF_30_CNT_SOCIAL_CIRCLE',
 'OBS_60_CNT_SOCIAL_CIRCLE',
 'DEF_60_CNT_SOCIAL_CIRCLE',
 'DAYS_LAST_PHONE_CHANGE',
 'FLAG_DOCUMENT_2',
 'FLAG_DOCUMENT_3',
 'FLAG_DOCUMENT_4',
 'FLAG_DOCUMENT_5',
 'FLAG_DOCUMENT_6',
 'FLAG_DOCUMENT_7',
 'FLAG_DOCUMENT_8',
 'FLAG_DOCUMENT_9',
 'FLAG_DOCUMENT_10',
 'FLAG_DOCUMENT_11',
 'FLAG_DOCUMENT_12',
 'FLAG_DOCUMENT_13',
 'FLAG_DOCUMENT_14',
 'FLAG_DOCUMENT_15',
 'FLAG_DOCUMENT_16',
 'FLAG_DOCUMENT_17',
 'FLAG_DOCUMENT_18',
 'FLAG_DOCUMENT_19',
 'FLAG_DOCUMENT_20',
 'FLAG_DOCUMENT_21',
 'AMT_REQ_CREDIT_BUREAU_HOUR',
 'AMT_REQ_CREDIT_BUREAU_DAY',
 'AMT_REQ_CREDIT_BUREAU_WEEK',
 'AMT_REQ_CREDIT_BUREAU_MON',
 'AMT_REQ_CREDIT_BUREAU_QRT',
 'AMT_REQ_CREDIT_BUREAU_YEAR']


def pct(x):
    return round(100*x,3)


del expLog
try:
    expLog
except NameError:
    expLog = pd.DataFrame(columns=["exp_name", 
                                   "Model_name",
                                   "Train Acc", 
                                   "Valid Acc",
                                   "Test  Acc",
                                   "Train F1", 
                                   "Valid F1",
                                   "Test F1",
                                   "Train AUC", 
                                   "Valid AUC",
                                   "Test  AUC",
                                   "Fit Time"
                                  ])
expLog


%%time 
np.random.seed(42)
full_pipeline_with_predictor = Pipeline([
        ("preparation", data_prep_pipeline),
        ("linear", LogisticRegression())
    ])
model = full_pipeline_with_predictor.fit(X_train, y_train)

CPU times: user 15.3 s, sys: 13.4 s, total: 28.6 s
Wall time: 5.22 s


from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, plot_roc_curve

np.round(accuracy_score(y_train, model.predict(X_train)), 3)

0.919


print('The Accuracy Score for Train Dataset:', np.round(accuracy_score(y_train, model.predict(X_train)), 3))
print('The F1 Score for Train Dataset:', np.round(f1_score(y_train, model.predict(X_train)), 3))
con_train = confusion_matrix(y_train, model.predict(X_train))
con_val = confusion_matrix(y_valid, model.predict(X_valid))
con_test = confusion_matrix(y_test, model.predict(X_test))
plt.figure(figsize=(7,6))
print('The Confusion Matrix for Training Set')
sns.heatmap(con_train, annot=True, fmt='g',cmap='viridis')
plt.show()
plt.figure(figsize=(7,6))
print('The Confusion Matrix for Validation Set')
sns.heatmap(con_val, annot=True, fmt='g',cmap='viridis')
plt.show()
plt.figure(figsize=(7,6))
print('The Confusion Matrix for Test Set')
sns.heatmap(con_test, annot=True, fmt='g',cmap='viridis')
plt.show()
plt.figure(figsize=(9,6))
print('The AUC-ROC for Train Set')
plot_roc_curve(model, X_train, y_train);
plt.show()
plt.figure(figsize=(9,6))
print('The AUC-ROC for Valid Set')
plot_roc_curve(model, X_valid, y_valid);
plt.show()
plt.figure(figsize=(10,8))
print('AUC-ROC for Test Set')
plot_roc_curve(model, X_test, y_test);
plt.show()

The Accuracy Score for Train Dataset: 0.919
The F1 Score for Train Dataset: 0.008
The Confusion Matrix for Training Set

The Confusion Matrix for Validation Set

The Confusion Matrix for Test Set

The AUC-ROC for Train Set

<Figure size 648x432 with 0 Axes>

The AUC-ROC for Valid Set

<Figure size 648x432 with 0 Axes>

AUC-ROC for Test Set

<Figure size 720x576 with 0 Axes>


#ROC auc score


from sklearn.metrics import roc_auc_score
roc_auc_score(y_train, model.predict_proba(X_train)[:, 1])

0.7376770013688367


exp_name = f"Baseline_{len(selected_features)}_features"
expLog.loc[len(expLog)] = [f"{exp_name}"] + [model_name] + list(np.round(
                   [accuracy_score(y_train, model.predict(X_train)), 
                    accuracy_score(y_valid, model.predict(X_valid)),
                    accuracy_score(y_test, model.predict(X_test)),
                    f1_score(y_train, model.predict(X_train)), 
                    f1_score(y_valid, model.predict(X_valid)),
                    f1_score(y_test, model.predict(X_test)),
                    roc_auc_score(y_train, model.predict_proba(X_train)[:, 1]),
                    roc_auc_score(y_valid, model.predict_proba(X_valid)[:, 1]),
                    roc_auc_score(y_test, model.predict_proba(X_test)[:, 1]),
                    fit_time], 4))
expLog


#importing requiered libarries
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
from sklearn.naive_bayes import GaussianNB
import time


expLog.shape[1]

12


clfs = [LogisticRegression(penalty='none'),
        LogisticRegression(penalty='l2'),
        DecisionTreeClassifier(),
        GaussianNB()]

for clf in clfs:
    start_time = time.time()
    full_pipeline_with_predictor = Pipeline([
        ("preparation", data_prep_pipeline),
        ("model", clf)
    ])
    model_name = "Baseline {}".format(type(full_pipeline_with_predictor['model']).__name__)
    model = full_pipeline_with_predictor.fit(X_train, y_train)
    fit_time = time.time() - start_time
    print('Fit Time for {} is: {} seconds'.format(model_name, fit_time))
    exp_name = f"Baseline_{len(selected_features)}_features"
    print(model_name)
    expLog.loc[len(expLog)] = [f"{exp_name}"] + [model_name] + list(np.round(
                   [accuracy_score(y_train, model.predict(X_train)), 
                    accuracy_score(y_valid, model.predict(X_valid)),
                    accuracy_score(y_test, model.predict(X_test)),
                    f1_score(y_train, model.predict(X_train)), 
                    f1_score(y_valid, model.predict(X_valid)),
                    f1_score(y_test, model.predict(X_test)),
                    roc_auc_score(y_train, model.predict_proba(X_train)[:, 1]),
                    roc_auc_score(y_valid, model.predict_proba(X_valid)[:, 1]),
                    roc_auc_score(y_test, model.predict_proba(X_test)[:, 1]),
                    fit_time], 4))
    con_train = confusion_matrix(y_train, model.predict(X_train))
    con_val = confusion_matrix(y_valid, model.predict(X_valid))
    con_test = confusion_matrix(y_test, model.predict(X_test))
    plt.figure(figsize=(7,6))
    print('Training Set Confusion Matrix')
    sns.heatmap(con_train, annot=True, fmt='g',cmap='viridis')
    plt.show()
    plt.figure(figsize=(7,6))
    print('Validation Set Confusion matrix')
    sns.heatmap(con_val, annot=True, fmt='g',cmap='viridis')
    plt.show()
    plt.figure(figsize=(7,6))
    print(' Test Set Confusion Matrix')
    sns.heatmap(con_test, annot=True, fmt='g',cmap='viridis')
    plt.show()
    plt.figure(figsize=(9,8))
    print('AUC-ROC for Train Set')
    plot_roc_curve(model, X_train, y_train);
    plt.show()
    plt.figure(figsize=(9,8))
    print('AUC-ROC for Valid Set')
    plot_roc_curve(model, X_valid, y_valid);
    plt.show()
    plt.figure(figsize=(9,8))
    print('AUC-ROC for Test Set')
    plot_roc_curve(model, X_test, y_test);
    plt.show()

expLog

Fit Time for Baseline LogisticRegression is: 5.874547004699707 seconds
Baseline LogisticRegression
Training Set Confusion Matrix

Validation Set Confusion matrix

 Test Set Confusion Matrix

AUC-ROC for Train Set

<Figure size 648x576 with 0 Axes>

AUC-ROC for Valid Set

<Figure size 648x576 with 0 Axes>

AUC-ROC for Test Set

<Figure size 648x576 with 0 Axes>

Fit Time for Baseline LogisticRegression is: 6.347308397293091 seconds
Baseline LogisticRegression
Training Set Confusion Matrix


#Random Forest Classifier


clfs = [RandomForestClassifier(), XGBClassifier()]

for clf in clfs:
    start_time = time.time()
    full_pipeline_with_predictor = Pipeline([
        ("preparation", data_prep_pipeline),
        ("model", clf)
    ])
    model_name = "Baseline {}".format(type(full_pipeline_with_predictor['model']).__name__)
    model = full_pipeline_with_predictor.fit(X_train, y_train)
    fit_time = time.time() - start_time
    print('Fit Time for {} is: {} seconds'.format(model_name, fit_time))
    exp_name = f"Baseline_{len(selected_features)}_features"
    print(model_name)
    exp_name = f"Baseline_{len(selected_features)}_features"
    expLog.loc[len(expLog)] = [f"{exp_name}"] + [model_name] + list(np.round(
                   [accuracy_score(y_train, model.predict(X_train)), 
                    accuracy_score(y_valid, model.predict(X_valid)),
                    accuracy_score(y_test, model.predict(X_test)),
                    f1_score(y_train, model.predict(X_train)), 
                    f1_score(y_valid, model.predict(X_valid)),
                    f1_score(y_test, model.predict(X_test)),
                    roc_auc_score(y_train, model.predict_proba(X_train)[:, 1]),
                    roc_auc_score(y_valid, model.predict_proba(X_valid)[:, 1]),
                    roc_auc_score(y_test, model.predict_proba(X_test)[:, 1]),
                    fit_time], 4))
 
    con_train = confusion_matrix(y_train, model.predict(X_train))
    con_val = confusion_matrix(y_valid, model.predict(X_valid))
    con_test = confusion_matrix(y_test, model.predict(X_test))
    plt.figure(figsize=(8,5))
    print('Confusion Matrix for Training Set')
    sns.heatmap(con_train, annot=True, fmt='g',cmap='viridis')
    plt.show()
    plt.figure(figsize=(8,5))
    print('Confusion Matrix for Validation Set')
    sns.heatmap(con_val, annot=True, fmt='g',cmap='viridis')
    plt.show()
    plt.figure(figsize=(8,5))
    print('Confusion Matrix for Test Set')
    sns.heatmap(con_test, annot=True, fmt='g',cmap='viridis')
    plt.show()
    plt.figure(figsize=(10,8))
    print('AUC-ROC for Train Set')
    plot_roc_curve(model, X_train, y_train);
    plt.show()
    plt.figure(figsize=(10,8))
    print('AUC-ROC for Valid Set')
    plot_roc_curve(model, X_valid, y_valid);
    plt.show()
    plt.figure(figsize=(10,8))
    print('AUC-ROC for Test Set')
    plot_roc_curve(model, X_test, y_test);
    plt.show()

Fit Time for Baseline RandomForestClassifier is: 136.4936501979828 seconds
Baseline RandomForestClassifier
Confusion Matrix for Training Set

Confusion Matrix for Validation Set

Confusion Matrix for Test Set

AUC-ROC for Train Set

<Figure size 720x576 with 0 Axes>

AUC-ROC for Valid Set

<Figure size 720x576 with 0 Axes>

AUC-ROC for Test Set

<Figure size 720x576 with 0 Axes>

[01:46:23] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
Fit Time for Baseline XGBClassifier is: 51.30178761482239 seconds
Baseline XGBClassifier
Confusion Matrix for Training Set


#Print the exp log


expLog


test_class_scores = model.predict_proba(X_kaggle_test)[:, 1]


test_class_scores[0:10]

array([0.02967482, 0.05190437, 0.02902925, 0.02359007, 0.09664427,
       0.03307424, 0.04967597, 0.02642321, 0.00492934, 0.08720874],
      dtype=float32)


# Submission dataframe
submit_df = datasets["application_test"][['SK_ID_CURR']]
submit_df['TARGET'] = test_class_scores

submit_df.head()


submit_df.to_csv("submission.csv",index=False)


! kaggle competitions submit -c home-credit-default-risk -f submission.csv -m "baseline submission"

Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.13 / client 1.5.12)
100%|█████████████████████████████████████████| 879k/879k [00:00<00:00, 989kB/s]
Successfully submitted to Home Credit Default Risk

	SK_ID_CURR	SK_ID_BUREAU	CREDIT_ACTIVE	CREDIT_CURRENCY	DAYS_CREDIT	DAYS_CREDIT_ENDDATE	DAYS_ENDDATE_FACT	AMT_CREDIT_MAX_OVERDUE	AMT_CREDIT_SUM	AMT_CREDIT_SUM_DEBT	AMT_CREDIT_SUM_LIMIT	CREDIT_TYPE	DAYS_CREDIT_UPDATE	AMT_ANNUITY
0	215354	5714462	Closed	currency 1	-497	-153.0	-153.0	NaN	91323.0	0.0	NaN	Consumer credit	-131	NaN
1	215354	5714463	Active	currency 1	-208	1075.0	NaN	NaN	225000.0	171342.0	NaN	Credit card	-20	NaN
2	215354	5714464	Active	currency 1	-203	528.0	NaN	NaN	464323.5	NaN	NaN	Consumer credit	-16	NaN
3	215354	5714465	Active	currency 1	-203	NaN	NaN	NaN	90000.0	NaN	NaN	Credit card	-16	NaN
4	215354	5714466	Active	currency 1	-629	1197.0	NaN	77674.5	2700000.0	NaN	NaN	Consumer credit	-21	NaN

	SK_ID_PREV	SK_ID_CURR	NUM_INSTALMENT_VERSION	NUM_INSTALMENT_NUMBER	DAYS_INSTALMENT	DAYS_ENTRY_PAYMENT	AMT_INSTALMENT	AMT_PAYMENT
0	1054186	161674	1.0	6	-1180.0	-1187.0	6948.360	6948.360
1	1330831	151639	0.0	34	-2156.0	-2156.0	1716.525	1716.525
2	2085231	193053	2.0	1	-63.0	-63.0	25425.000	25425.000
3	2452527	199697	1.0	3	-2418.0	-2426.0	24350.130	24350.130
4	2714724	167756	1.0	2	-1383.0	-1366.0	2165.040	2160.585

	SK_ID_PREV	SK_ID_CURR	NAME_CONTRACT_TYPE	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	...	NAME_SELLER_INDUSTRY	CNT_PAYMENT	NAME_YIELD_GROUP	PRODUCT_COMBINATION	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
0	2030495	271877	Consumer loans	1730.430	17145.0	17145.0	0.0	17145.0	SATURDAY	15	...	Connectivity	12.0	middle	POS mobile with interest	365243.0	-42.0	300.0	-42.0	-37.0	0.0
1	2802425	108129	Cash loans	25188.615	607500.0	679671.0	NaN	607500.0	THURSDAY	11	...	XNA	36.0	low_action	Cash X-Sell: low	365243.0	-134.0	916.0	365243.0	365243.0	1.0
2	2523466	122040	Cash loans	15060.735	112500.0	136444.5	NaN	112500.0	TUESDAY	11	...	XNA	12.0	high	Cash X-Sell: high	365243.0	-271.0	59.0	365243.0	365243.0	1.0
3	2819243	176158	Cash loans	47041.335	450000.0	470790.0	NaN	450000.0	MONDAY	7	...	XNA	12.0	middle	Cash X-Sell: middle	365243.0	-482.0	-152.0	-182.0	-177.0	1.0
4	1784265	202054	Cash loans	31924.395	337500.0	404055.0	NaN	337500.0	THURSDAY	9	...	XNA	24.0	high	Cash Street: high	NaN	NaN	NaN	NaN	NaN	NaN

	SK_ID_PREV	SK_ID_CURR	MONTHS_BALANCE	CNT_INSTALMENT	CNT_INSTALMENT_FUTURE	NAME_CONTRACT_STATUS
0	1803195	182943	-31	48.0	45.0	Active
1	1715348	367990	-33	36.0	35.0	Active
2	1784872	397406	-32	12.0	9.0	Active
3	1903291	269225	-35	48.0	42.0	Active
4	2341044	334279	-35	36.0	35.0	Active

	SK_ID_CURR	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	...	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
count	48744.000000	48744.000000	4.874400e+04	4.874400e+04	48720.000000	4.874400e+04	48744.000000	48744.000000	48744.000000	48744.000000	...	48744.000000	48744.0	48744.0	48744.0	42695.000000	42695.000000	42695.000000	42695.000000	42695.000000	42695.000000
mean	277796.676350	0.397054	1.784318e+05	5.167404e+05	29426.240209	4.626188e+05	0.021226	-16068.084605	67485.366322	-4967.652716	...	0.001559	0.0	0.0	0.0	0.002108	0.001803	0.002787	0.009299	0.546902	1.983769
std	103169.547296	0.709047	1.015226e+05	3.653970e+05	16016.368315	3.367102e+05	0.014428	4325.900393	144348.507136	3552.612035	...	0.039456	0.0	0.0	0.0	0.046373	0.046132	0.054037	0.110924	0.693305	1.838873
min	100001.000000	0.000000	2.694150e+04	4.500000e+04	2295.000000	4.500000e+04	0.000253	-25195.000000	-17463.000000	-23722.000000	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	188557.750000	0.000000	1.125000e+05	2.606400e+05	17973.000000	2.250000e+05	0.010006	-19637.000000	-2910.000000	-7459.250000	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	277549.000000	0.000000	1.575000e+05	4.500000e+05	26199.000000	3.960000e+05	0.018850	-15785.000000	-1293.000000	-4490.000000	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000
75%	367555.500000	1.000000	2.250000e+05	6.750000e+05	37390.500000	6.300000e+05	0.028663	-12496.000000	-296.000000	-1901.000000	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	1.000000	3.000000
max	456250.000000	20.000000	4.410000e+06	2.245500e+06	180576.000000	2.245500e+06	0.072508	-7338.000000	365243.000000	0.000000	...	1.000000	0.0	0.0	0.0	2.000000	2.000000	2.000000	6.000000	7.000000	17.000000

	SK_ID_CURR	TARGET	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	...	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
0	100002	1	Cash loans	M	N	Y	202500.0	406597.5	24700.5	...	0.0	0.0	0.0	0.0	0.0	1.0
1	100003	0	Cash loans	F	N	N	270000.0	1293502.5	35698.5	...	0.0	0.0	0.0	0.0	0.0	0.0
2	100004	0	Revolving loans	M	Y	Y	67500.0	135000.0	6750.0	...	0.0	0.0	0.0	0.0	0.0	0.0
3	100006	0	Cash loans	F	N	Y	135000.0	312682.5	29686.5	...	NaN	NaN	NaN	NaN	NaN	NaN
4	100007	0	Cash loans	M	N	Y	121500.0	513000.0	21865.5	...	0.0	0.0	0.0	0.0	0.0	0.0

	SK_ID_CURR	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	...	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
0	100001	Cash loans	F	N	Y	0	135000.0	568800.0	20560.5	450000.0	...	0.0	0.0	0.0	0.0	0.0	0.0
1	100005	Cash loans	M	N	Y	0	99000.0	222768.0	17370.0	180000.0	...	0.0	0.0	0.0	0.0	0.0	3.0
2	100013	Cash loans	M	Y	Y	0	202500.0	663264.0	69777.0	630000.0	...	0.0	0.0	0.0	0.0	1.0	4.0
3	100028	Cash loans	F	N	Y	2	315000.0	1575000.0	49018.5	1575000.0	...	0.0	0.0	0.0	0.0	0.0	3.0
4	100038	Cash loans	M	Y	N	1	180000.0	625500.0	32067.0	625500.0	...	NaN	NaN	NaN	NaN	NaN	NaN

	SK_ID_PREV	SK_ID_CURR	MONTHS_BALANCE	AMT_BALANCE	AMT_CREDIT_LIMIT_ACTUAL	AMT_DRAWINGS_ATM_CURRENT	AMT_DRAWINGS_CURRENT	AMT_DRAWINGS_POS_CURRENT	AMT_INST_MIN_REGULARITY	...	AMT_RECIVABLE	AMT_TOTAL_RECEIVABLE	CNT_DRAWINGS_ATM_CURRENT	CNT_DRAWINGS_CURRENT	CNT_DRAWINGS_POS_CURRENT	CNT_INSTALMENT_MATURE_CUM	NAME_CONTRACT_STATUS
0	2562384	378907	-6	56.970	135000	0.0	877.5	877.5	1700.325	...	0.000	0.000	0.0	1	1.0	35.0	Active
1	2582071	363914	-1	63975.555	45000	2250.0	2250.0	0.0	2250.000	...	64875.555	64875.555	1.0	1	0.0	69.0	Active
2	1740877	371185	-7	31815.225	450000	0.0	0.0	0.0	2250.000	...	31460.085	31460.085	0.0	0	0.0	30.0	Active
3	1389973	337855	-4	236572.110	225000	2250.0	2250.0	0.0	11795.760	...	233048.970	233048.970	1.0	1	0.0	10.0	Active
4	1891521	126868	-1	453919.455	450000	0.0	11547.0	11547.0	22924.890	...	453919.455	453919.455	0.0	1	1.0	101.0	Active

	SK_ID_CURR	TARGET	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	...	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
count	307511.000000	307511.000000	307511.000000	3.075110e+05	3.075110e+05	307499.000000	3.072330e+05	307511.000000	307511.000000	307511.000000	...	307511.000000	307511.000000	307511.000000	307511.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000
mean	278180.518577	0.080729	0.417052	1.687979e+05	5.990260e+05	27108.573909	5.383962e+05	0.020868	-16036.995067	63815.045904	...	0.008130	0.000595	0.000507	0.000335	0.006402	0.007000	0.034362	0.267395	0.265474	1.899974
std	102790.175348	0.272419	0.722121	2.371231e+05	4.024908e+05	14493.737315	3.694465e+05	0.013831	4363.988632	141275.766519	...	0.089798	0.024387	0.022518	0.018299	0.083849	0.110757	0.204685	0.916002	0.794056	1.869295
min	100002.000000	0.000000	0.000000	2.565000e+04	4.500000e+04	1615.500000	4.050000e+04	0.000290	-25229.000000	-17912.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	189145.500000	0.000000	0.000000	1.125000e+05	2.700000e+05	16524.000000	2.385000e+05	0.010006	-19682.000000	-2760.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	278202.000000	0.000000	0.000000	1.471500e+05	5.135310e+05	24903.000000	4.500000e+05	0.018850	-15750.000000	-1213.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
75%	367142.500000	0.000000	1.000000	2.025000e+05	8.086500e+05	34596.000000	6.795000e+05	0.028663	-12413.000000	-289.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000
max	456255.000000	1.000000	19.000000	1.170000e+08	4.050000e+06	258025.500000	4.050000e+06	0.072508	-7489.000000	365243.000000	...	1.000000	1.000000	1.000000	1.000000	4.000000	9.000000	8.000000	27.000000	261.000000	25.000000

	SK_ID_CURR	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	...	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
count	48744.000000	48744	48744	48744	48744	48744.000000	4.874400e+04	4.874400e+04	48720.000000	4.874400e+04	...	48744.000000	48744.0	48744.0	48744.0	42695.000000	42695.000000	42695.000000	42695.000000	42695.000000	42695.000000
unique	NaN	2	2	2	2	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
top	NaN	Cash loans	F	N	Y	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
freq	NaN	48305	32678	32311	33658	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
mean	277796.676350	NaN	NaN	NaN	NaN	0.397054	1.784318e+05	5.167404e+05	29426.240209	4.626188e+05	...	0.001559	0.0	0.0	0.0	0.002108	0.001803	0.002787	0.009299	0.546902	1.983769
std	103169.547296	NaN	NaN	NaN	NaN	0.709047	1.015226e+05	3.653970e+05	16016.368315	3.367102e+05	...	0.039456	0.0	0.0	0.0	0.046373	0.046132	0.054037	0.110924	0.693305	1.838873
min	100001.000000	NaN	NaN	NaN	NaN	0.000000	2.694150e+04	4.500000e+04	2295.000000	4.500000e+04	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	188557.750000	NaN	NaN	NaN	NaN	0.000000	1.125000e+05	2.606400e+05	17973.000000	2.250000e+05	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	277549.000000	NaN	NaN	NaN	NaN	0.000000	1.575000e+05	4.500000e+05	26199.000000	3.960000e+05	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000
75%	367555.500000	NaN	NaN	NaN	NaN	1.000000	2.250000e+05	6.750000e+05	37390.500000	6.300000e+05	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	1.000000	3.000000
max	456250.000000	NaN	NaN	NaN	NaN	20.000000	4.410000e+06	2.245500e+06	180576.000000	2.245500e+06	...	1.000000	0.0	0.0	0.0	2.000000	2.000000	2.000000	6.000000	7.000000	17.000000

	Percent	Train Missing Count
COMMONAREA_MEDI	69.87	214865
COMMONAREA_AVG	69.87	214865
COMMONAREA_MODE	69.87	214865
NONLIVINGAPARTMENTS_MODE	69.43	213514
NONLIVINGAPARTMENTS_AVG	69.43	213514
NONLIVINGAPARTMENTS_MEDI	69.43	213514
FONDKAPREMONT_MODE	68.39	210295
LIVINGAPARTMENTS_MODE	68.35	210199
LIVINGAPARTMENTS_AVG	68.35	210199
LIVINGAPARTMENTS_MEDI	68.35	210199
FLOORSMIN_AVG	67.85	208642
FLOORSMIN_MODE	67.85	208642
FLOORSMIN_MEDI	67.85	208642
YEARS_BUILD_MEDI	66.50	204488
YEARS_BUILD_MODE	66.50	204488
YEARS_BUILD_AVG	66.50	204488
OWN_CAR_AGE	65.99	202929
LANDAREA_MEDI	59.38	182590
LANDAREA_MODE	59.38	182590
LANDAREA_AVG	59.38	182590

	Percent	Test Missing Count
COMMONAREA_AVG	68.72	33495
COMMONAREA_MODE	68.72	33495
COMMONAREA_MEDI	68.72	33495
NONLIVINGAPARTMENTS_AVG	68.41	33347
NONLIVINGAPARTMENTS_MODE	68.41	33347
NONLIVINGAPARTMENTS_MEDI	68.41	33347
FONDKAPREMONT_MODE	67.28	32797
LIVINGAPARTMENTS_AVG	67.25	32780
LIVINGAPARTMENTS_MODE	67.25	32780
LIVINGAPARTMENTS_MEDI	67.25	32780
FLOORSMIN_MEDI	66.61	32466
FLOORSMIN_AVG	66.61	32466
FLOORSMIN_MODE	66.61	32466
OWN_CAR_AGE	66.29	32312
YEARS_BUILD_AVG	65.28	31818
YEARS_BUILD_MEDI	65.28	31818
YEARS_BUILD_MODE	65.28	31818
LANDAREA_MEDI	57.96	28254
LANDAREA_AVG	57.96	28254
LANDAREA_MODE	57.96	28254

	TARGET	AMT_CREDIT	AMT_INCOME_TOTAL	AMT_GOODS_PRICE
TARGET	1.000000	-0.143445	-0.079451	-0.153852
AMT_CREDIT	-0.143445	1.000000	0.355705	0.999502
AMT_INCOME_TOTAL	-0.079451	0.355705	1.000000	0.358367
AMT_GOODS_PRICE	-0.153852	0.999502	0.358367	1.000000

	EXT_SOURCE_3	EXT_SOURCE_2	EXT_SOURCE_1	DAYS_BIRTH
EXT_SOURCE_3	1.000000	0.261958	0.473936	-0.474564
EXT_SOURCE_2	0.261958	1.000000	0.476205	-0.229976
EXT_SOURCE_1	0.473936	0.476205	1.000000	-0.847116
DAYS_BIRTH	-0.474564	-0.229976	-0.847116	1.000000

	TARGET	DAYS_BIRTH	YEARS_BIRTH	GROUPED_YEARS_BIRTH
0	1	-9461	25.920548	(25.0, 30.0]
1	0	-16765	45.931507	(45.0, 50.0]
2	0	-19046	52.180822	(50.0, 55.0]
3	0	-19005	52.068493	(50.0, 55.0]
4	0	-19932	54.608219	(50.0, 55.0]
5	0	-16941	46.413699	(45.0, 50.0]
6	0	-13778	37.747945	(35.0, 40.0]
7	0	-18850	51.643836	(50.0, 55.0]
8	0	-20099	55.065753	(55.0, 60.0]
9	0	-14469	39.641096	(35.0, 40.0]
10	0	-10197	27.936986	(25.0, 30.0]
11	0	-20417	55.936986	(55.0, 60.0]
12	0	-13439	36.819178	(35.0, 40.0]
13	0	-14086	38.591781	(35.0, 40.0]
14	0	-14583	39.953425	(35.0, 40.0]
15	0	-8728	23.912329	(20.0, 25.0]
16	0	-12931	35.427397	(35.0, 40.0]
17	0	-9776	26.783562	(25.0, 30.0]
18	0	-17718	48.542466	(45.0, 50.0]
19	0	-11348	31.090411	(30.0, 35.0]

	SK_ID_BUREAU	MONTHS_BALANCE	STATUS
0	5715448	0	C
1	5715448	-1	C
2	5715448	-2	C
3	5715448	-3	C
4	5715448	-4	C

	TARGET	DAYS_BIRTH	YEARS_BIRTH
GROUPED_YEARS_BIRTH
(20.0, 25.0]	0.123036	-8532.795625	23.377522
(25.0, 30.0]	0.111436	-10155.219250	27.822518
(30.0, 35.0]	0.102814	-11854.848377	32.479037
(35.0, 40.0]	0.089414	-13707.908253	37.555913
(40.0, 45.0]	0.078491	-15497.661233	42.459346
(45.0, 50.0]	0.074171	-17323.900441	47.462741
(50.0, 55.0]	0.066968	-19196.494791	52.593136
(55.0, 60.0]	0.055314	-20984.262742	57.491131
(60.0, 65.0]	0.052737	-22780.547460	62.412459
(65.0, 70.0]	0.037270	-24292.614340	66.555108

	A	B	C
0	1	1	0.586963
1	1	2	-0.368022
2	2	3	-0.224149
3	2	4	-0.355347

	SK_ID_CURR	SK_ID_PREV	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	HOUR_APPR_PROCESS_START	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	...	RATE_INTEREST_PRIVILEGED	DAYS_DECISION	SELLERPLACE_AREA	CNT_PAYMENT	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
0	100001	1.369693e+06	3951.000	24835.50	23787.00	2520.0	24835.5	13.000000	1.0	0.104326	...	NaN	-1740.0	23.0	8.0	365243.0	-1709.000000	-1499.000000	-1619.000000	-1612.000000	0.000000
1	100002	1.038818e+06	9251.775	179055.00	179055.00	0.0	179055.0	9.000000	1.0	0.000000	...	NaN	-606.0	500.0	24.0	365243.0	-565.000000	125.000000	-25.000000	-17.000000	0.000000
2	100003	2.281150e+06	56553.990	435436.50	484191.00	3442.5	435436.5	14.666667	1.0	0.050030	...	NaN	-1305.0	533.0	10.0	365243.0	-1274.333333	-1004.333333	-1054.333333	-1047.333333	0.666667
3	100004	1.564014e+06	5357.250	24282.00	20106.00	4860.0	24282.0	5.000000	1.0	0.212008	...	NaN	-815.0	30.0	4.0	365243.0	-784.000000	-694.000000	-724.000000	-714.000000	0.000000
4	100005	2.176837e+06	4813.200	22308.75	20076.75	4464.0	44617.5	10.500000	1.0	0.108964	...	NaN	-536.0	18.0	12.0	365243.0	-706.000000	-376.000000	-466.000000	-460.000000	0.000000

	exp_name	Model_name	Train Acc	Valid Acc	Test Acc	Train F1	Valid F1	Test F1	Train AUC	Valid AUC	Test AUC	Fit Time
0	Baseline_67_features	Baseline XGBClassifier	0.9193	0.9193	0.9195	0.0081	0.0069	0.0099	0.7377	0.73	0.7385	49.3522
1	Baseline_67_features	Baseline XGBClassifier	0.9193	0.9193	0.9195	0.0081	0.0069	0.0099	0.7377	0.73	0.7385	49.3522

	SK_ID_CURR	TARGET
0	100001	0.029675
1	100005	0.051904
2	100013	0.029029
3	100028	0.023590
4	100038	0.096644

Home Credit Default Risk (HCDR)¶

Some of the challenges¶

Kaggle API setup¶

Dataset and how to download¶

Back ground Home Credit Group¶

Home Credit Group¶

Background on the dataset¶

Data files overview¶

Table sizes¶

Downloading the files via Kaggle API¶

Imports¶

Data files overview¶

Data Dictionary¶

Application train¶

Application test¶

The Other datasets¶

Exploratory Data Analysis¶

Summary of Application train¶

Missing data for application train¶

Distribution of the target column¶

Correlation with the target column¶

Applicants Age¶

Applicants occupations¶

Dataset questions¶

Unique record for each SK_ID_CURR¶

previous applications for the submission file¶

Histogram of Number of previous applications for an ID¶

Can we differentiate applications by low, medium and high previous apps?¶

Joining secondary tables with the primary table¶

Joining previous_application with application_x¶

Roadmap for secondary table processing¶

agg detour¶

Multiple condition expressions in Pandas¶

Missing values in prevApps¶

feature engineering for prevApp table¶

Fixing Column names after Pandas agg() function to summarize grouped data¶

feature transformer for prevApp table¶

Join the labeled dataset¶

Join the unlabeled dataset (i.e., the submission file)¶

Processing pipeline¶

OHE when previously unseen unique values in the test/validation set¶

HCDR preprocessing¶

Baseline Model¶

Evaluation metrics¶

Submission File Prep¶

Kaggle submission via the command line API¶

report submission¶

Write-up¶

Abstract¶

Introduction¶

Feature Engineering and transformers¶

Pipelines¶

Experimental results¶

Discussion¶

Conclusion¶

Kaggle Submission¶

Discussion¶

Conclusion¶

References¶

TODO: Predicting Loan Repayment with Automated Feature Engineering in Featuretools¶

Joining `previous_application` with `application_x`¶